Assignment
sn0wfree
12/16/2016
Import Data
crowdfunding<-read.csv( "forqrm.csv" ,header=1)
crowdfunding_region<-read.csv( "forqrm.csv" ,header=1)
head(crowdfunding)library(lmtest)Loading required package: zoo
Attaching package: 'zoo'
The following objects are masked from 'package:base':
as.Date, as.Date.numericrownames(crowdfunding)<-crowdfunding$State
se<-crowdfunding[c("GiniCoeff","pAdDeg","successful_rate")]
se#corFind Topics
1.Geography information
- Geography information:found the significally different by state/by region
- Amount
- successfull rate within graphy/plot
2.Factors analysis:
- factors:studying the relationship between Successful Rate and other factors:
- Higher Eduction:pAdDeg;
- Ginicoeff # average_pledged_amount_of_Grand.Total#
pie(table(crowdfunding_region$Region))
3.Total regression
successful_rate2GiniCoeff<-lm(crowdfunding$successful_rate~crowdfunding$GiniCoeff)#significant:0.00398
summary(successful_rate2GiniCoeff)
Call:
lm(formula = crowdfunding$successful_rate ~ crowdfunding$GiniCoeff)
Residuals:
Min 1Q Median 3Q Max
-0.197648 -0.054730 -0.003818 0.055170 0.201729
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.5417 0.2993 -1.810 0.07655 .
crowdfunding$GiniCoeff 2.0008 0.6613 3.026 0.00398 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.08254 on 48 degrees of freedom
Multiple R-squared: 0.1602, Adjusted R-squared: 0.1427
F-statistic: 9.154 on 1 and 48 DF, p-value: 0.003981successful_rate2NPov000s<-lm(crowdfunding$successful_rate~crowdfunding$NPov000s)#0.231
summary(successful_rate2NPov000s)
Call:
lm(formula = crowdfunding$successful_rate ~ crowdfunding$NPov000s)
Residuals:
Min 1Q Median 3Q Max
-0.230673 -0.041789 -0.001671 0.045678 0.193621
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.541e-01 1.642e-02 21.560 <2e-16 ***
crowdfunding$NPov000s 9.537e-06 1.113e-05 0.857 0.396
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.08938 on 48 degrees of freedom
Multiple R-squared: 0.01507, Adjusted R-squared: -0.005451
F-statistic: 0.7343 on 1 and 48 DF, p-value: 0.3957summary(lm(crowdfunding$successful_rate~crowdfunding$pHigh))#bad:0.2320
Call:
lm(formula = crowdfunding$successful_rate ~ crowdfunding$pHigh)
Residuals:
Min 1Q Median 3Q Max
-0.225211 -0.041414 -0.003402 0.046713 0.203517
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.7499 0.3198 2.345 0.0232 *
crowdfunding$pHigh -0.4451 0.3677 -1.211 0.2320
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.08872 on 48 degrees of freedom
Multiple R-squared: 0.02963, Adjusted R-squared: 0.00941
F-statistic: 1.465 on 1 and 48 DF, p-value: 0.232summary(lm(crowdfunding$successful_rate~crowdfunding$pBatDeg))#low:0.05511
Call:
lm(formula = crowdfunding$successful_rate ~ crowdfunding$pBatDeg)
Residuals:
Min 1Q Median 3Q Max
-0.23906 -0.06503 0.01415 0.05343 0.16307
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.22503 0.07128 3.157 0.00275 **
crowdfunding$pBatDeg 0.50740 0.25810 1.966 0.05511 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.08665 on 48 degrees of freedom
Multiple R-squared: 0.07451, Adjusted R-squared: 0.05523
F-statistic: 3.865 on 1 and 48 DF, p-value: 0.05511successful_rate2pAdDeg<-lm(crowdfunding$successful_rate~crowdfunding$pAdDeg)#significant:0.00501
summary(successful_rate2pAdDeg)
Call:
lm(formula = crowdfunding$successful_rate ~ crowdfunding$pAdDeg)
Residuals:
Min 1Q Median 3Q Max
-0.22412 -0.06718 0.01152 0.04708 0.15306
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.22570 0.04815 4.688 2.31e-05 ***
crowdfunding$pAdDeg 1.40251 0.47679 2.942 0.00501 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.0829 on 48 degrees of freedom
Multiple R-squared: 0.1527, Adjusted R-squared: 0.1351
F-statistic: 8.653 on 1 and 48 DF, p-value: 0.005015#supplement regression
summary(lm(crowdfunding$GiniCoeff~crowdfunding$pAdDeg))#significant:0.0353
Call:
lm(formula = crowdfunding$GiniCoeff ~ crowdfunding$pAdDeg)
Residuals:
Min 1Q Median 3Q Max
-0.031714 -0.014215 0.001214 0.013231 0.037791
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.431222 0.009986 43.184 <2e-16 ***
crowdfunding$pAdDeg 0.214191 0.098889 2.166 0.0353 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.01719 on 48 degrees of freedom
Multiple R-squared: 0.08904, Adjusted R-squared: 0.07006
F-statistic: 4.691 on 1 and 48 DF, p-value: 0.03531summary(lm(crowdfunding$count_of_Grand.Total~crowdfunding$Pop2010))#significant:0.0353
Call:
lm(formula = crowdfunding$count_of_Grand.Total ~ crowdfunding$Pop2010)
Residuals:
Min 1Q Median 3Q Max
-696.93 -96.93 -0.61 74.90 1418.91
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.129e+02 5.125e+01 -2.203 0.0325 *
crowdfunding$Pop2010 5.039e-05 5.569e-06 9.048 6.03e-12 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 268 on 48 degrees of freedom
Multiple R-squared: 0.6304, Adjusted R-squared: 0.6227
F-statistic: 81.87 on 1 and 48 DF, p-value: 6.028e-12#summary(lm(successful_rate2PovRate1$residuals~crowdfunding$pAdDeg))#0.0003881
#summary(lm(successful_rate2PovRate1$residuals~crowdfunding$GiniCoeff))#0.0264
summary(lm(successful_rate2GiniCoeff$residuals~crowdfunding$pAdDeg))#residuals ~ ADdeg:0.0368
Call:
lm(formula = successful_rate2GiniCoeff$residuals ~ crowdfunding$pAdDeg)
Residuals:
Min 1Q Median 3Q Max
-0.187967 -0.048021 -0.005676 0.052160 0.167582
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.09539 0.04578 -2.083 0.0426 *
crowdfunding$pAdDeg 0.97396 0.45341 2.148 0.0368 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.07884 on 48 degrees of freedom
Multiple R-squared: 0.0877, Adjusted R-squared: 0.06869
F-statistic: 4.614 on 1 and 48 DF, p-value: 0.03678#summary(lm(successful_rate2GiniCoeff$residuals~crowdfunding$PovRate1))#residuals ~ PovRate1:0.771
#summary(lm(successful_rate2pAdDeg$residuals~crowdfunding$PovRate1))#0.004505
summary(lm(successful_rate2pAdDeg$residuals~crowdfunding$GiniCoeff))#0.0295
Call:
lm(formula = successful_rate2pAdDeg$residuals ~ crowdfunding$GiniCoeff)
Residuals:
Min 1Q Median 3Q Max
-0.195484 -0.051186 0.004758 0.054168 0.147777
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.6411 0.2860 -2.242 0.0296 *
crowdfunding$GiniCoeff 1.4178 0.6319 2.244 0.0295 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.07887 on 48 degrees of freedom
Multiple R-squared: 0.09492, Adjusted R-squared: 0.07606
F-statistic: 5.034 on 1 and 48 DF, p-value: 0.02951#summary(lm(log(crowdfunding$average_pledged_amount_of_Grand.Total)~crowdfunding$GiniCoeff))#0.02357Geography information
Geography information-preprocess:kmean
hc<-hclust(dist(crowdfunding),method = "ward.D", members = NULL)NAs introduced by coercionplclust(hc)'plclust' is deprecated.
Use 'plot' instead.
See help("Deprecated")rect.hclust(hc,k=2)
heatmap(as.matrix(dist(crowdfunding,method= 'euclidean')),labRow = F, labCol = F)NAs introduced by coercion
result<-cutree(hc,k=2)
result.category<-as.data.frame(result)
colnames(result.category)<-c("MDS_Category")
result.categorypie(result)
barplot(result,col =result )
#table(result)
#summary(result)
plot(result,type = "p",col=result,xlab="State",xaxt="n",ylab="MDS_Category")
library(ggplot2)package 'ggplot2' was built under R version 3.3.2mds2 <- -cmdscale(dist(crowdfunding))NAs introduced by coercionplot(mds2, type="n", axes=FALSE, ann=FALSE)
text(mds2, labels=rownames(mds2), xpd = NA)
mds<-cmdscale(dist(crowdfunding),k=2,eig=T)NAs introduced by coercionx = mds$points[,1]
y = mds$points[,2]
p=ggplot(data.frame(x,y),aes(x,y))
p+geom_point(size=5 , alpha=0.8 , aes(colour=factor(result) ))
NA#regroup-------------------------------
#crowdfunding.backup<-crowdfunding
result.category["State"]<-row.names(result.category)
#crowdfunding<-crowdfunding[c("State","successful_rate","Region","count_of_Grand.Total","GiniCoeff","pAdDeg","pHigh","pBatDeg","Pop2010")]
#crowdfunding$Pop2010<-log(crowdfunding$Pop2010)
#crowdfunding$count_of_Grand.Total<-log(crowdfunding$count_of_Grand.Total)
crowdfunding<-merge(crowdfunding,result.category,all.x=TRUE)
#row.names(crowdfunding)<-crowdfunding$State
#GiniCoeff-------
par(mfrow=c(1,2))
plot(crowdfunding$successful_rate[crowdfunding$MDS_Category==1]~crowdfunding$GiniCoeff[crowdfunding$MDS_Category==1])
points(lowess(crowdfunding$successful_rate[crowdfunding$MDS_Category==1]~crowdfunding$GiniCoeff[crowdfunding$MDS_Category==1]),type="l",col="red")plot(crowdfunding$successful_rate[crowdfunding$MDS_Category==2]~crowdfunding$GiniCoeff[crowdfunding$MDS_Category==2])
points(lowess(crowdfunding$successful_rate[crowdfunding$MDS_Category==2]~crowdfunding$GiniCoeff[crowdfunding$MDS_Category==2]),type="l",col="red")
summary(lm(crowdfunding$successful_rate[crowdfunding$MDS_Category==1]~crowdfunding$GiniCoeff[crowdfunding$MDS_Category==1]))
Call:
lm(formula = crowdfunding$successful_rate[crowdfunding$MDS_Category ==
1] ~ crowdfunding$GiniCoeff[crowdfunding$MDS_Category ==
1])
Residuals:
Min 1Q Median 3Q Max
-0.198796 -0.051943 -0.002387 0.054209 0.197760
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.6421 0.3803 -1.689 0.0999 .
crowdfunding$GiniCoeff[crowdfunding$MDS_Category == 1] 2.2359 0.8487 2.634 0.0123 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.08777 on 36 degrees of freedom
Multiple R-squared: 0.1616, Adjusted R-squared: 0.1383
F-statistic: 6.94 on 1 and 36 DF, p-value: 0.01235summary(lm(crowdfunding$successful_rate[crowdfunding$MDS_Category==2]~crowdfunding$GiniCoeff[crowdfunding$MDS_Category==2]))
Call:
lm(formula = crowdfunding$successful_rate[crowdfunding$MDS_Category ==
2] ~ crowdfunding$GiniCoeff[crowdfunding$MDS_Category ==
2])
Residuals:
Min 1Q Median 3Q Max
-0.128261 -0.041117 -0.009601 0.055870 0.085466
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.8124 0.7597 -1.069 0.310
crowdfunding$GiniCoeff[crowdfunding$MDS_Category == 2] 2.5483 1.6283 1.565 0.149
Residual standard error: 0.067 on 10 degrees of freedom
Multiple R-squared: 0.1967, Adjusted R-squared: 0.1164
F-statistic: 2.449 on 1 and 10 DF, p-value: 0.1486#pAdDeg-------
plot(crowdfunding$successful_rate[crowdfunding$MDS_Category==1]~crowdfunding$pAdDeg[crowdfunding$MDS_Category==1])
points(lowess(crowdfunding$successful_rate[crowdfunding$MDS_Category==1]~crowdfunding$pAdDeg[crowdfunding$MDS_Category==1]),type="l",col="red")plot(crowdfunding$successful_rate[crowdfunding$MDS_Category==2]~crowdfunding$pAdDeg[crowdfunding$MDS_Category==2])
points(lowess(crowdfunding$successful_rate[crowdfunding$MDS_Category==2]~crowdfunding$pAdDeg[crowdfunding$MDS_Category==2]),type="l",col="red")
summary(lm(crowdfunding$successful_rate[crowdfunding$MDS_Category==1]~crowdfunding$pAdDeg[crowdfunding$MDS_Category==1]))
Call:
lm(formula = crowdfunding$successful_rate[crowdfunding$MDS_Category ==
1] ~ crowdfunding$pAdDeg[crowdfunding$MDS_Category == 1])
Residuals:
Min 1Q Median 3Q Max
-0.22501 -0.06782 0.01676 0.05208 0.15686
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.24079 0.05685 4.236 0.000151 ***
crowdfunding$pAdDeg[crowdfunding$MDS_Category == 1] 1.24109 0.57707 2.151 0.038292 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.09023 on 36 degrees of freedom
Multiple R-squared: 0.1139, Adjusted R-squared: 0.08924
F-statistic: 4.625 on 1 and 36 DF, p-value: 0.03829summary(lm(crowdfunding$successful_rate[crowdfunding$MDS_Category==2]~crowdfunding$pAdDeg[crowdfunding$MDS_Category==2]))
Call:
lm(formula = crowdfunding$successful_rate[crowdfunding$MDS_Category ==
2] ~ crowdfunding$pAdDeg[crowdfunding$MDS_Category == 2])
Residuals:
Min 1Q Median 3Q Max
-0.09739 -0.03931 0.00642 0.04437 0.07161
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.13748 0.09196 1.495 0.1658
crowdfunding$pAdDeg[crowdfunding$MDS_Category == 2] 2.23782 0.84798 2.639 0.0248 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.05739 on 10 degrees of freedom
Multiple R-squared: 0.4105, Adjusted R-squared: 0.3516
F-statistic: 6.964 on 1 and 10 DF, p-value: 0.02478Geography Information-simpleplot
par(mfrow=c(1,2) )
#count_of_Grand.Total
plot(crowdfunding$count_of_Grand.Total,col=crowdfunding$Region, main="Count of Projects Plot",ylab="Count of Projects",xaxt="n",xlab="State")
#axis(side=1,at=c(1,2,3,4,5,6,7,8),labels=c(crowdfunding$State))
legend("topright",legend = levels(crowdfunding$Region),cex = 0.8, pch = 1,col=1:4)#successful_rate
plot(crowdfunding$successful_rate,col=crowdfunding$Region, main="Successful Rate Plot",ylab="Successful Rate",xaxt="n",xlab="State")
#axis(side=1,at=c(1,2,3,4,5,6,7,8),labels=c(crowdfunding$State))
legend("topleft",legend = levels(crowdfunding$Region),cex = 0.8, pch = 1,col=1:4)
Geography information-boxplot
par(mfrow=c(1,3))
#Boxplot for successful_rate and count_of_Grand.Total
#count_of_Grand.Total
boxplot(crowdfunding$count_of_Grand.Total[crowdfunding$Region=="Midwest"],
crowdfunding$count_of_Grand.Total[crowdfunding$Region=="Northeast"],
crowdfunding$count_of_Grand.Total[crowdfunding$Region=="South"],
crowdfunding$count_of_Grand.Total[crowdfunding$Region=="West"],
names=levels(crowdfunding$Region),main="Count of Projects BoxPlot by Region",varwidth=TRUE)
require(moments)Loading required package: momentsskewness(crowdfunding_region$count_of_Grand.Total[crowdfunding_region$Region=="Midwest"])[1] 1.547595skewness(crowdfunding_region$count_of_Grand.Total[crowdfunding_region$Region=="Northeast"])[1] 2.368424skewness(crowdfunding_region$count_of_Grand.Total[crowdfunding_region$Region=="South"])[1] 1.217785skewness(crowdfunding_region$count_of_Grand.Total[crowdfunding_region$Region=="West"])[1] 3.007817kurtosis(crowdfunding_region$count_of_Grand.Total[crowdfunding_region$Region=="Midwest"])[1] 4.72236kurtosis(crowdfunding_region$count_of_Grand.Total[crowdfunding_region$Region=="Northeast"])[1] 6.812673kurtosis(crowdfunding_region$count_of_Grand.Total[crowdfunding_region$Region=="South"])[1] 3.431574kurtosis(crowdfunding_region$count_of_Grand.Total[crowdfunding_region$Region=="West"])[1] 10.40857boxplot(log(crowdfunding_region$count_of_Grand.Total[crowdfunding_region$Region=="Midwest"]),
log(crowdfunding_region$count_of_Grand.Total[crowdfunding_region$Region=="Northeast"]),
log(crowdfunding_region$count_of_Grand.Total[crowdfunding_region$Region=="South"]),
log(crowdfunding_region$count_of_Grand.Total[crowdfunding_region$Region=="West"]),
names=levels(crowdfunding$Region),main="Log Count of Projects BoxPlot by Region")#successful_rate
boxplot(crowdfunding$successful_rate[crowdfunding$Region=="Midwest"],
crowdfunding$successful_rate[crowdfunding$Region=="Northeast"],
crowdfunding$successful_rate[crowdfunding$Region=="South"],
crowdfunding$successful_rate[crowdfunding$Region=="West"],
names=levels(crowdfunding$Region),main="Successful Rate BoxPlot by Region")
skewness(crowdfunding_region$successful_rate[crowdfunding_region$Region=="Midwest"])[1] -0.6469442skewness(crowdfunding_region$successful_rate[crowdfunding_region$Region=="Northeast"])[1] -0.1748926skewness(crowdfunding_region$successful_rate[crowdfunding_region$Region=="South"])[1] -0.3792879skewness(crowdfunding_region$successful_rate[crowdfunding_region$Region=="West"])[1] -0.8937484kurtosis(crowdfunding_region$successful_rate[crowdfunding_region$Region=="Midwest"])[1] 2.9282kurtosis(crowdfunding_region$successful_rate[crowdfunding_region$Region=="Northeast"])[1] 1.466362kurtosis(crowdfunding_region$successful_rate[crowdfunding_region$Region=="South"])[1] 2.493975kurtosis(crowdfunding_region$successful_rate[crowdfunding_region$Region=="West"])[1] 3.664296par(mfrow=c(2,4))
#count_of_Grand.Total
qqnorm(crowdfunding$count_of_Grand.Total[crowdfunding$Region=="Midwest"],xlab="Midwest",xaxt="n",col="black",main="Count of Projects-Norm Q-Q Plot")
qqline(crowdfunding$count_of_Grand.Total[crowdfunding$Region=="Midwest"])qqnorm(crowdfunding$count_of_Grand.Total[crowdfunding$Region=="Northeast"],xlab="Northeast",xaxt="n",col="red",main="Count of Projectse-Norm Q-Q Plot")
qqline(crowdfunding$count_of_Grand.Total[crowdfunding$Region=="Northeast"])qqnorm(crowdfunding$count_of_Grand.Total[crowdfunding$Region=="South"],xlab="South",col="green",xaxt="n",main="Count of Projects-Norm Q-Q Plot")
qqline(crowdfunding$count_of_Grand.Total[crowdfunding$Region=="South"])qqnorm(crowdfunding$count_of_Grand.Total[crowdfunding$Region=="West"],xlab="West",col="blue",xaxt="n",main="Count of Projects-Norm Q-Q Plot")
qqline(crowdfunding$count_of_Grand.Total[crowdfunding$Region=="West"])#successful_rate
qqnorm(crowdfunding$successful_rate[crowdfunding$Region=="Midwest"],xlab="Midwest",col="black",xaxt="n",main="Successful Rate-Norm Q-Q Plot")
qqline(crowdfunding$successful_rate[crowdfunding$Region=="Midwest"])qqnorm(crowdfunding$successful_rate[crowdfunding$Region=="Northeast"],xlab="Northeast",col="red",xaxt="n",main="Successful Rate-Norm Q-Q Plot")
qqline(crowdfunding$successful_rate[crowdfunding$Region=="Northeast"])qqnorm(crowdfunding$successful_rate[crowdfunding$Region=="South"],xlab="South",col="green",xaxt="n",main="Successful Rate-Norm Q-Q Plot")
qqline(crowdfunding$successful_rate[crowdfunding$Region=="South"])qqnorm(crowdfunding$successful_rate[crowdfunding$Region=="West"],xlab="West",col="blue",xaxt="n",main="Successful Rate-Norm Q-Q Plot")
qqline(crowdfunding$successful_rate[crowdfunding$Region=="West"])
kurtosis(crowdfunding$successful_rate[crowdfunding$Region=="Midwest"])[1] 2.9282kurtosis(crowdfunding$successful_rate[crowdfunding$Region=="Northeast"]) [1] 1.466362kurtosis(crowdfunding$successful_rate[crowdfunding$Region=="South"]) [1] 2.493975kurtosis(crowdfunding$successful_rate[crowdfunding$Region=="West"]) [1] 3.664296par(mfrow=c(1,3))
qqnorm(crowdfunding$successful_rate, main ="Successful rate:Q-Q norm",col="red")
qqline(crowdfunding$successful_rate)qqnorm(crowdfunding$GiniCoeff, main ="GiniCoeff:Q-Q norm",col="blue")
qqline(crowdfunding$GiniCoeff)qqnorm(crowdfunding$pAdDeg, main ="pAdDeg:Q-Q norm",col="green")
qqline(crowdfunding$pAdDeg)
cor.test(crowdfunding$successful_rate,crowdfunding$GiniCoeff)
Pearson's product-moment correlation
data: crowdfunding$successful_rate and crowdfunding$GiniCoeff
t = 3.0255, df = 48, p-value = 0.003981
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
0.1371263 0.6105363
sample estimates:
cor
0.4001989 cor.test(crowdfunding$successful_rate,crowdfunding$pAdDeg,method="spearman")Cannot compute exact p-value with ties
Spearman's rank correlation rho
data: crowdfunding$successful_rate and crowdfunding$pAdDeg
S = 12634, p-value = 0.004719
alternative hypothesis: true rho is not equal to 0
sample estimates:
rho
0.3933076 Geography information:factors
boxplot(crowdfunding$GiniCoeff[crowdfunding$Region=="Midwest"],crowdfunding$GiniCoeff[crowdfunding$Region=="Northeast"],crowdfunding$GiniCoeff[crowdfunding$Region=="South"],crowdfunding$GiniCoeff[crowdfunding$Region=="West"],names=levels(crowdfunding$Region),main="GiniCoeff BoxPlot by Region",varwidth=TRUE)
boxplot(crowdfunding$pAdDeg[crowdfunding$Region=="Midwest"],crowdfunding$pAdDeg[crowdfunding$Region=="Northeast"],crowdfunding$pAdDeg[crowdfunding$Region=="South"],crowdfunding$pAdDeg[crowdfunding$Region=="West"],names=levels(crowdfunding$Region),main="pAdDeg BoxPlot by Region",varwidth=TRUE)
summary(lm(crowdfunding$successful_rate[crowdfunding$Region=="Midwest"]~crowdfunding$GiniCoeff[crowdfunding$Region=="Midwest"]))
Call:
lm(formula = crowdfunding$successful_rate[crowdfunding$Region ==
"Midwest"] ~ crowdfunding$GiniCoeff[crowdfunding$Region ==
"Midwest"])
Residuals:
Min 1Q Median 3Q Max
-0.159935 -0.042151 0.001007 0.042888 0.126971
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.9201 0.9832 -1.953 0.0794 .
crowdfunding$GiniCoeff[crowdfunding$Region == "Midwest"] 5.1042 2.2205 2.299 0.0444 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.08428 on 10 degrees of freedom
Multiple R-squared: 0.3457, Adjusted R-squared: 0.2803
F-statistic: 5.284 on 1 and 10 DF, p-value: 0.04435par(mfrow=c(1,4))
#GiniCoeff---------
plot(crowdfunding$successful_rate[crowdfunding$Region=="Midwest"]~crowdfunding$GiniCoeff[crowdfunding$Region=="Midwest"],xlab="GiniCoeff in Midwest",ylab="Successful rate in Midwest")
points(lowess(crowdfunding$successful_rate[crowdfunding$Region=="Midwest"]~crowdfunding$GiniCoeff[crowdfunding$Region=="Midwest"]),col="red",type="l")plot(crowdfunding$successful_rate[crowdfunding$Region=="West"]~crowdfunding$GiniCoeff[crowdfunding$Region=="West"],xlab="GiniCoeff in West",ylab="Successful rate in West")
points(lowess(crowdfunding$successful_rate[crowdfunding$Region=="West"]~crowdfunding$GiniCoeff[crowdfunding$Region=="West"]),col="red",type="l")plot(crowdfunding$successful_rate[crowdfunding$Region=="South"]~crowdfunding$GiniCoeff[crowdfunding$Region=="South"],xlab="GiniCoeff in South",ylab="Successful rate in South")
points(lowess(crowdfunding$successful_rate[crowdfunding$Region=="South"]~crowdfunding$GiniCoeff[crowdfunding$Region=="South"]),col="red",type="l")plot(crowdfunding$successful_rate[crowdfunding$Region=="Northeast"]~crowdfunding$GiniCoeff[crowdfunding$Region=="Northeast"],xlab="GiniCoeff in Northeast",ylab="Successful rate in Northeast")
points(lowess(crowdfunding$successful_rate[crowdfunding$Region=="Northeast"]~crowdfunding$GiniCoeff[crowdfunding$Region=="Northeast"]),col="red",type="l")
#pAdDeg-----------
plot(crowdfunding$successful_rate[crowdfunding$Region=="Midwest"]~crowdfunding$pAdDeg[crowdfunding$Region=="Midwest"],xlab="pAdDeg in Midwest",ylab="Successful rate in Midwest")
points(lowess(crowdfunding$successful_rate[crowdfunding$Region=="Midwest"]~crowdfunding$pAdDeg[crowdfunding$Region=="Midwest"]),col="red",type="l")plot(crowdfunding$successful_rate[crowdfunding$Region=="West"]~crowdfunding$pAdDeg[crowdfunding$Region=="West"],xlab="pAdDeg in West",ylab="Successful rate in West")
points(lowess(crowdfunding$successful_rate[crowdfunding$Region=="West"]~crowdfunding$pAdDeg[crowdfunding$Region=="West"]),col="red",type="l")plot(crowdfunding$successful_rate[crowdfunding$Region=="South"]~crowdfunding$pAdDeg[crowdfunding$Region=="South"],xlab="pAdDeg in South",ylab="Successful rate in South")
points(lowess(crowdfunding$successful_rate[crowdfunding$Region=="South"]~crowdfunding$pAdDeg[crowdfunding$Region=="South"]),col="red",type="l")plot(crowdfunding$successful_rate[crowdfunding$Region=="Northeast"]~crowdfunding$pAdDeg[crowdfunding$Region=="Northeast"],xlab="pAdDeg in Northeast",ylab="Successful rate in Northeast")
points(lowess(crowdfunding$successful_rate[crowdfunding$Region=="Northeast"]~crowdfunding$pAdDeg[crowdfunding$Region=="Northeast"]),col="red",type="l")
#pBatDeg-----------
plot(crowdfunding$successful_rate[crowdfunding$Region=="Midwest"]~crowdfunding$pBatDeg[crowdfunding$Region=="Midwest"],xlab="pBatDeg in Midwest",ylab="Successful rate in Midwest")
points(lowess(crowdfunding$successful_rate[crowdfunding$Region=="Midwest"]~crowdfunding$pBatDeg[crowdfunding$Region=="Midwest"]),col="red",type="l")plot(crowdfunding$successful_rate[crowdfunding$Region=="West"]~crowdfunding$pBatDeg[crowdfunding$Region=="West"],xlab="pBatDeg in West",ylab="Successful rate in West")
points(lowess(crowdfunding$successful_rate[crowdfunding$Region=="West"]~crowdfunding$pBatDeg[crowdfunding$Region=="West"]),col="red",type="l")plot(crowdfunding$successful_rate[crowdfunding$Region=="South"]~crowdfunding$pBatDeg[crowdfunding$Region=="South"],xlab="pBatDeg in South",ylab="Successful rate in South")
points(lowess(crowdfunding$successful_rate[crowdfunding$Region=="South"]~crowdfunding$pBatDeg[crowdfunding$Region=="South"]),col="red",type="l")plot(crowdfunding$successful_rate[crowdfunding$Region=="Northeast"]~crowdfunding$pBatDeg[crowdfunding$Region=="Northeast"],xlab="pBatDeg in Northeast",ylab="Successful rate in Northeast")
points(lowess(crowdfunding$successful_rate[crowdfunding$Region=="Northeast"]~crowdfunding$pBatDeg[crowdfunding$Region=="Northeast"]),col="red",type="l")
#pHigh-----------
plot(crowdfunding$successful_rate[crowdfunding$Region=="Midwest"]~crowdfunding$pHigh[crowdfunding$Region=="Midwest"],xlab="pHigh in Midwest",ylab="Successful rate in Midwest")
points(lowess(crowdfunding$successful_rate[crowdfunding$Region=="Midwest"]~crowdfunding$pHigh[crowdfunding$Region=="Midwest"]),col="red",type="l")plot(crowdfunding$successful_rate[crowdfunding$Region=="West"]~crowdfunding$pHigh[crowdfunding$Region=="West"],xlab="pHigh in West",ylab="Successful rate in West")
points(lowess(crowdfunding$successful_rate[crowdfunding$Region=="West"]~crowdfunding$pHigh[crowdfunding$Region=="West"]),col="red",type="l")plot(crowdfunding$successful_rate[crowdfunding$Region=="South"]~crowdfunding$pHigh[crowdfunding$Region=="South"],xlab="pHigh in South",ylab="Successful rate in South")
points(lowess(crowdfunding$successful_rate[crowdfunding$Region=="South"]~crowdfunding$pHigh[crowdfunding$Region=="South"]),col="red",type="l")plot(crowdfunding$successful_rate[crowdfunding$Region=="Northeast"]~crowdfunding$pHigh[crowdfunding$Region=="Northeast"],xlab="pHigh in Northeast",ylab="Successful rate in Northeast")
points(lowess(crowdfunding$successful_rate[crowdfunding$Region=="Northeast"]~crowdfunding$pHigh[crowdfunding$Region=="Northeast"]),col="red",type="l")
par(mfrow=c(1,4))
#GiniCoeff---------
plot(crowdfunding$successful_rate[crowdfunding$Region=="Midwest"]~crowdfunding$GiniCoeff[crowdfunding$Region=="Midwest"],xlab="GiniCoeff in Midwest",ylab="Successful rate in Midwest")
points(lowess(crowdfunding$successful_rate[crowdfunding$Region=="Midwest"]~crowdfunding$GiniCoeff[crowdfunding$Region=="Midwest"]),col="red",type="l")plot(crowdfunding$successful_rate[crowdfunding$Region=="West"]~crowdfunding$GiniCoeff[crowdfunding$Region=="West"],xlab="GiniCoeff in West",ylab="Successful rate in West")
points(lowess(crowdfunding$successful_rate[crowdfunding$Region=="West"]~crowdfunding$GiniCoeff[crowdfunding$Region=="West"]),col="red",type="l")plot(crowdfunding$successful_rate[crowdfunding$Region=="South"]~crowdfunding$GiniCoeff[crowdfunding$Region=="South"],xlab="GiniCoeff in South",ylab="Successful rate in South")
points(lowess(crowdfunding$successful_rate[crowdfunding$Region=="South"]~crowdfunding$GiniCoeff[crowdfunding$Region=="South"]),col="red",type="l")plot(crowdfunding$successful_rate[crowdfunding$Region=="Northeast"]~crowdfunding$GiniCoeff[crowdfunding$Region=="Northeast"],xlab="GiniCoeff in Northeast",ylab="Successful rate in Northeast")
points(lowess(crowdfunding$successful_rate[crowdfunding$Region=="Northeast"]~crowdfunding$GiniCoeff[crowdfunding$Region=="Northeast"]),col="red",type="l")
#pAdDeg-----------
plot(crowdfunding$successful_rate[crowdfunding$Region=="Midwest"]~crowdfunding$pAdDeg[crowdfunding$Region=="Midwest"],xlab="pAdDeg in Midwest",ylab="Successful rate in Midwest")
points(lowess(crowdfunding$successful_rate[crowdfunding$Region=="Midwest"]~crowdfunding$pAdDeg[crowdfunding$Region=="Midwest"]),col="red",type="l")plot(crowdfunding$successful_rate[crowdfunding$Region=="West"]~crowdfunding$pAdDeg[crowdfunding$Region=="West"],xlab="pAdDeg in West",ylab="Successful rate in West")
points(lowess(crowdfunding$successful_rate[crowdfunding$Region=="West"]~crowdfunding$pAdDeg[crowdfunding$Region=="West"]),col="red",type="l")plot(crowdfunding$successful_rate[crowdfunding$Region=="South"]~crowdfunding$pAdDeg[crowdfunding$Region=="South"],xlab="pAdDeg in South",ylab="Successful rate in South")
points(lowess(crowdfunding$successful_rate[crowdfunding$Region=="South"]~crowdfunding$pAdDeg[crowdfunding$Region=="South"]),col="red",type="l")plot(crowdfunding$successful_rate[crowdfunding$Region=="Northeast"]~crowdfunding$pAdDeg[crowdfunding$Region=="Northeast"],xlab="pAdDeg in Northeast",ylab="Successful rate in Northeast")
points(lowess(crowdfunding$successful_rate[crowdfunding$Region=="Northeast"]~crowdfunding$pAdDeg[crowdfunding$Region=="Northeast"]),col="red",type="l")
#pBatDeg-----------
plot(crowdfunding$successful_rate[crowdfunding$Region=="Midwest"]~crowdfunding$pBatDeg[crowdfunding$Region=="Midwest"],xlab="pBatDeg in Midwest",ylab="Successful rate in Midwest")
points(lowess(crowdfunding$successful_rate[crowdfunding$Region=="Midwest"]~crowdfunding$pBatDeg[crowdfunding$Region=="Midwest"]),col="red",type="l")plot(crowdfunding$successful_rate[crowdfunding$Region=="West"]~crowdfunding$pBatDeg[crowdfunding$Region=="West"],xlab="pBatDeg in West",ylab="Successful rate in West")
points(lowess(crowdfunding$successful_rate[crowdfunding$Region=="West"]~crowdfunding$pBatDeg[crowdfunding$Region=="West"]),col="red",type="l")plot(crowdfunding$successful_rate[crowdfunding$Region=="South"]~crowdfunding$pBatDeg[crowdfunding$Region=="South"],xlab="pBatDeg in South",ylab="Successful rate in South")
points(lowess(crowdfunding$successful_rate[crowdfunding$Region=="South"]~crowdfunding$pBatDeg[crowdfunding$Region=="South"]),col="red",type="l")plot(crowdfunding$successful_rate[crowdfunding$Region=="Northeast"]~crowdfunding$pBatDeg[crowdfunding$Region=="Northeast"],xlab="pBatDeg in Northeast",ylab="Successful rate in Northeast")
points(lowess(crowdfunding$successful_rate[crowdfunding$Region=="Northeast"]~crowdfunding$pBatDeg[crowdfunding$Region=="Northeast"]),col="red",type="l")
#pHigh-----------
plot(crowdfunding$successful_rate[crowdfunding$Region=="Midwest"]~crowdfunding$pHigh[crowdfunding$Region=="Midwest"],xlab="pHigh in Midwest",ylab="Successful rate in Midwest")
points(lowess(crowdfunding$successful_rate[crowdfunding$Region=="Midwest"]~crowdfunding$pHigh[crowdfunding$Region=="Midwest"]),col="red",type="l")plot(crowdfunding$successful_rate[crowdfunding$Region=="West"]~crowdfunding$pHigh[crowdfunding$Region=="West"],xlab="pHigh in West",ylab="Successful rate in West")
points(lowess(crowdfunding$successful_rate[crowdfunding$Region=="West"]~crowdfunding$pHigh[crowdfunding$Region=="West"]),col="red",type="l")plot(crowdfunding$successful_rate[crowdfunding$Region=="South"]~crowdfunding$pHigh[crowdfunding$Region=="South"],xlab="pHigh in South",ylab="Successful rate in South")
points(lowess(crowdfunding$successful_rate[crowdfunding$Region=="South"]~crowdfunding$pHigh[crowdfunding$Region=="South"]),col="red",type="l")plot(crowdfunding$successful_rate[crowdfunding$Region=="Northeast"]~crowdfunding$pHigh[crowdfunding$Region=="Northeast"],xlab="pHigh in Northeast",ylab="Successful rate in Northeast")
points(lowess(crowdfunding$successful_rate[crowdfunding$Region=="Northeast"]~crowdfunding$pHigh[crowdfunding$Region=="Northeast"]),col="red",type="l")
Geography information-t.test
#t.test(crowdfunding$successful_rate[crowdfunding$Region=="West"],crowdfunding$successful_rate[crowdfunding$Region=="Northeast"])
#t.test(crowdfunding$count_of_Grand.Total[crowdfunding$Region=="West"],crowdfunding$count_of_Grand.Total[crowdfunding$Region=="Northeast"])
#calcualte P Value in the t.test of Successful Rate by Region
p=NULL
temp<-NULL
for (location1 in c(levels(crowdfunding$Region))){
for (location2 in c(levels(crowdfunding$Region))){
if (1){
temp<-t.test(crowdfunding$successful_rate[crowdfunding$Region==location1],crowdfunding$successful_rate[crowdfunding$Region==location2])
if(temp$p.value<=0.1){
#print(c(location1,location2,temp$p.value))
}
p<-c(p,temp$p.value)}}}
SR.t.test.p.vlaue<-as.data.frame(matrix(p,4,4),row.names = c(levels(crowdfunding$Region)))
colnames(SR.t.test.p.vlaue)<-c(levels(crowdfunding$Region))
print("Successful Rate by Region")[1] "Successful Rate by Region"SR.t.test.p.vlaue#--------------------------------------------------------
#calcualte P Value in the t.test of Count of projects by Region
p=NULL
temp<-NULL
for (location1 in c(levels(crowdfunding$Region))){
for (location2 in c(levels(crowdfunding$Region))){
if (1){
temp<-t.test(log(crowdfunding$count_of_Grand.Total[crowdfunding$Region==location1]),log(crowdfunding$count_of_Grand.Total[crowdfunding$Region==location2]))
if(temp$p.value<=0.1){
#print(c(location1,location2,temp$p.value))
}
p<-c(p,temp$p.value)}}}
CP.t.test.p.vlaue<-as.data.frame(matrix(p,4,4),row.names = c(levels(crowdfunding$Region)))
colnames(CP.t.test.p.vlaue)<-c(levels(crowdfunding$Region))
print("Count of Projects by Region ")[1] "Count of Projects by Region "CP.t.test.p.vlaue#--------------------------------------------------------
#calcualte P Value in the t.test of Count of projects by kmeans
"MDS-Category"[1] "MDS-Category"p=NULL
temp<-NULL
#for (location1 in 1:4){
# for (location2 in 1:4){
# if (1){
# temp<-t.test(crowdfunding$successful_rate[crowdfunding$MDS_Category==location1],crowdfunding$successful_rate[crowdfunding$MDS_Category==location2])
# if(temp$p.value<=0.1){
# print(c(location1,location2,temp$p.value))
# }
# p<-c(p,temp$p.value)}}}
#MDSR.t.test.p.vlaue<-as.data.frame(matrix(p,4,4),row.names = c(1:4))
#colnames(MDSR.t.test.p.vlaue)<-c(1:4)
#print("Successful Rate by Region")
#MDSR.t.test.p.vlaueGeography information-t-bootstrap
wilcox.test(Northeast.SR.bootstrap,West.SR.bootstrap)
Wilcoxon rank sum test with continuity correction
data: Northeast.SR.bootstrap and West.SR.bootstrap
W = 994490, p-value < 2.2e-16
alternative hypothesis: true location shift is not equal to 0wilcox.test(Northeast.SR.bootstrap,South.SR.bootstrap)
Wilcoxon rank sum test with continuity correction
data: Northeast.SR.bootstrap and South.SR.bootstrap
W = 988070, p-value < 2.2e-16
alternative hypothesis: true location shift is not equal to 0wilcox.test(Northeast.SR.bootstrap,Midwest.SR.bootstrap)
Wilcoxon rank sum test with continuity correction
data: Northeast.SR.bootstrap and Midwest.SR.bootstrap
W = 990780, p-value < 2.2e-16
alternative hypothesis: true location shift is not equal to 0wilcox.test(West.SR.bootstrap,Midwest.SR.bootstrap)
Wilcoxon rank sum test with continuity correction
data: West.SR.bootstrap and Midwest.SR.bootstrap
W = 471150, p-value = 0.02549
alternative hypothesis: true location shift is not equal to 0wilcox.test(West.SR.bootstrap,South.SR.bootstrap)
Wilcoxon rank sum test with continuity correction
data: West.SR.bootstrap and South.SR.bootstrap
W = 207160, p-value < 2.2e-16
alternative hypothesis: true location shift is not equal to 0wilcox.test(Midwest.SR.bootstrap,South.SR.bootstrap)
Wilcoxon rank sum test with continuity correction
data: Midwest.SR.bootstrap and South.SR.bootstrap
W = 260520, p-value < 2.2e-16
alternative hypothesis: true location shift is not equal to 0Geography information-wilcox.test
wilcox.test(crowdfunding$successful_rate[crowdfunding$Region=="West"],crowdfunding$successful_rate[crowdfunding$Region=="Northeast"])
Wilcoxon rank sum test
data: crowdfunding$successful_rate[crowdfunding$Region == "West"] and crowdfunding$successful_rate[crowdfunding$Region == "Northeast"]
W = 29, p-value = 0.05135
alternative hypothesis: true location shift is not equal to 0wilcox.test(crowdfunding$successful_rate[crowdfunding$Region=="Midwest"],crowdfunding$successful_rate[crowdfunding$Region=="Northeast"])
Wilcoxon rank sum test
data: crowdfunding$successful_rate[crowdfunding$Region == "Midwest"] and crowdfunding$successful_rate[crowdfunding$Region == "Northeast"]
W = 28, p-value = 0.06928
alternative hypothesis: true location shift is not equal to 0wilcox.test(crowdfunding$successful_rate[crowdfunding$Region=="South"],crowdfunding$successful_rate[crowdfunding$Region=="Northeast"])cannot compute exact p-value with ties
Wilcoxon rank sum test with continuity correction
data: crowdfunding$successful_rate[crowdfunding$Region == "South"] and crowdfunding$successful_rate[crowdfunding$Region == "Northeast"]
W = 43, p-value = 0.1066
alternative hypothesis: true location shift is not equal to 0wilcox.test(crowdfunding$successful_rate[crowdfunding$Region=="West"],crowdfunding$successful_rate[crowdfunding$Region=="Midwest"])cannot compute exact p-value with ties
Wilcoxon rank sum test with continuity correction
data: crowdfunding$successful_rate[crowdfunding$Region == "West"] and crowdfunding$successful_rate[crowdfunding$Region == "Midwest"]
W = 75, p-value = 0.8918
alternative hypothesis: true location shift is not equal to 0Factors analysis
This article is to analyse the factors to the crowdfunding successful rate. I guess the Education, the inequity of family income and the poverty rate may be related to the crowdfunding successful rate. and in the follow context, i will analyse the those factors.
Firstly, The Statistical Summary ### Factors Analysis-Statistical Summary
library(moments)
summary(crowdfunding$successful_rate) Min. 1st Qu. Median Mean 3rd Qu. Max.
0.1250 0.3179 0.3636 0.3631 0.4095 0.5484 kurtosis(crowdfunding$successful_rate)[1] 3.630147summary(crowdfunding$GiniCoeff) Min. 1st Qu. Median Mean 3rd Qu. Max.
0.4190 0.4400 0.4530 0.4522 0.4658 0.4990 kurtosis(crowdfunding$GiniCoeff)[1] 2.552647summary(crowdfunding$pAdDeg) Min. 1st Qu. Median Mean 3rd Qu. Max.
0.06100 0.07950 0.09200 0.09794 0.11000 0.16400 kurtosis(crowdfunding$pAdDeg)[1] 3.382781summary(crowdfunding$PovRate1) Min. 1st Qu. Median Mean 3rd Qu. Max.
0.0920 0.1212 0.1480 0.1480 0.1705 0.2190 kurtosis(crowdfunding$PovRate1)[1] 2.159154Factors Analysis-Plot for Factors
boxplot(crowdfunding$successful_rate,crowdfunding$GiniCoeff,crowdfunding$pAdDeg,crowdfunding$PovRate1,names = c("Successful Rate","GiniCoeff","Higher Education","PovRate1"),main="Factors Box Plot")
par(mfrow=c(1,4))
plot(crowdfunding$successful_rate,col=crowdfunding$Region,pch=10,cex=1.5,xlab="State",ylab="Successful Rate",xaxt="n",main="Successful Rate Plot with lowess line")
points(lowess(crowdfunding$successful),type="l")legend("topleft",legend = levels(crowdfunding$Region),cex = 0.8, pch = 1,col=1:4)
plot(crowdfunding$GiniCoeff,col=crowdfunding$Region,pch=18,cex=1.5,xlab="State",xaxt="n",ylab="GiniCoeff ",xaxt="n",main="GiniCoeff Plot with lowess line")points(lowess(crowdfunding$GiniCoeff),type="l")
legend("topleft",legend = levels(crowdfunding$Region),cex = 0.8, pch = 1,col=1:4)plot(crowdfunding$pAdDeg,col=crowdfunding$Region,pch=15,cex=1.5,xlab="State",xaxt="n",ylab="Adanced Education Rate",xaxt="n",main="Adanced Education Rate Plot with lowess line")
points(lowess(crowdfunding$pAdDeg),type="l")legend("topright",legend = levels(crowdfunding$Region),cex = 0.8, pch = 1,col=1:4)
plot(crowdfunding$pAdDeg,col=crowdfunding$Region,pch=15,cex=1.5,xlab="State",xaxt="n",ylab="Adanced Education Rate",xaxt="n",main="Adanced Education Rate Plot with lowess line")points(lowess(crowdfunding$pAdDeg),type="l")
legend("topright",legend = levels(crowdfunding$Region),cex = 0.8, pch = 1,col=1:4)
require(car)Loading required package: car
package 'car' was built under R version 3.3.2scatterplotMatrix(~successful_rate+PovRate1+pAdDeg,data=crowdfunding,spread=1,upper.panel=NULL,main="Scatter Plot Matrix on Factors")
require(moments)
kurtosis(crowdfunding$pAdDeg)[1] 3.382781#plot(crowdfunding$PovRate1,col="black",pch=16,xlab="State",xaxt="n",ylab="Poverty Rate",xaxt="n",main="Poverty Rate Plot")
#library(gridExtra)
summary(lm(log(successful_rate)~log(Collectivism),data=crowdfunding))
Call:
lm(formula = log(successful_rate) ~ log(Collectivism), data = crowdfunding)
Residuals:
Min 1Q Median 3Q Max
-0.88745 -0.11958 0.01185 0.12325 0.51358
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.7061 0.7085 -3.819 0.000384 ***
log(Collectivism) 0.4259 0.1819 2.341 0.023406 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2802 on 48 degrees of freedom
Multiple R-squared: 0.1025, Adjusted R-squared: 0.08381
F-statistic: 5.483 on 1 and 48 DF, p-value: 0.02341ggplot(crowdfunding,aes(y=successful_rate,x=Collectivism))+geom_point()+geom_smooth(method="lm")
library(car)
scatterplot(crowdfunding$successful_rate,log(crowdfunding$average_of_goal_Grand.Total),pch=19)
scatterplot(crowdfunding$successful_rate~crowdfunding$GiniCoeff,pch=19)
scatterplot(crowdfunding$successful_rate~crowdfunding$PovRate1,pch=19)
scatterplot(crowdfunding$successful_rate~crowdfunding$Densitym2,pch=19)
scatterplot(crowdfunding$successful_rate~crowdfunding$pHigh,pch=19)
scatterplot(crowdfunding$successful_rate~crowdfunding$pBatDeg,pch=19)
scatterplot(crowdfunding$successful_rate~crowdfunding$pAdDeg,pch=19)
Factors Analysis-Successful Rate|PovRate1
#redo scatterplot with Successful Rate-PovRate1
scatterplot(crowdfunding$successful_rate,crowdfunding$PovRate1,pch=19)
#anova(successful_rate2PovRate1)
library(ggplot2)
ggplot(crowdfunding,aes(x=PovRate1,y=successful_rate,main = "Successful rate~PovRate"))+geom_point(aes(col=Region))+geom_smooth(method = "loess")+ggtitle("Successful Rate-PovRate1 Plot")+theme(plot.title=element_text(hjust=0.5))
par(mfrow=c(1,2))
boxplot(crowdfunding$successful_rate,crowdfunding$PovRate1,names=c("Successful Rate","PovRate1"))
boxplot(crowdfunding$successful_rate[crowdfunding$PovRate1>mean(crowdfunding$PovRate1)],crowdfunding$successful_rate[crowdfunding$PovRate1<=mean(crowdfunding$PovRate1)],col = c("green","deepskyblue"),names=c("Successful%(High PovRate)","Successful%(Low PovRate)"),xlab="Successful rate by PovRate1")
t.test(crowdfunding$successful_rate[crowdfunding$PovRate1>mean(crowdfunding$PovRate1)],crowdfunding$successful_rate[crowdfunding$PovRate1<=mean(crowdfunding$PovRate1)])
Welch Two Sample t-test
data: crowdfunding$successful_rate[crowdfunding$PovRate1 > mean(crowdfunding$PovRate1)] and crowdfunding$successful_rate[crowdfunding$PovRate1 <= mean(crowdfunding$PovRate1)]
t = -0.01904, df = 43.704, p-value = 0.9849
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.05105839 0.05010288
sample estimates:
mean of x mean of y
0.3628157 0.3632935 plot(crowdfunding$PovRate1,crowdfunding$successful_rate,pch=19,col=crowdfunding$Region,ylab="Successful Rate",xlab="PovRate1",main="Successful Rate-PovRate1 Plot with lowess line")
points(lowess(crowdfunding$PovRate1,crowdfunding$successful_rate,f=1/3),pch=4,col="orange",type="l")#abline(lm(crowdfunding$successful_rate~crowdfunding$PovRate1),col="orange")
legend("bottomright",legend = levels(crowdfunding$Region),cex = 0.8, pch = 19,col=1:4)
qqplot(crowdfunding$PovRate1,crowdfunding$successful_rate,pch=1,col=crowdfunding$Region,main="QQ plot: (Successful Rate & PovRate)")qqline(crowdfunding$PovRate1 ,crowdfunding$successful_rate,col="red")the condition has length > 1 and only the first element will be usedlegend("topleft",legend = levels(crowdfunding$Region), pch = 19,col=1:3)par(mfrow=c(1,1))
#qqnorm(crowdfunding$successful_rate,col=crowdfunding$Region,xlab="Successful Rate")
#qqline(crowdfunding$successful_rate,col="red")
qqnorm(crowdfunding$PovRate1,col=crowdfunding$Region,pch=18,xlab ="PovRate1")
qqline(crowdfunding$PovRate1,col="red")
require(ggplot2)
ggplot(crowdfunding,aes(x=PovRate1,y=successful_rate,main = "Successful rate~PovRate1"))+geom_point(aes(group=1,col=Region))+geom_smooth(method = "loess",se=FALSE,aes(group=2,col="Loess Line"),lty="dashed")+geom_smooth(method = "lm",aes(group=2,col="Regression Line"),cex=0.8,lty=1)+ggtitle("Successful Rate-PovRate1 Plot with Loess line and OLS-estimated line")+labs(x="PovRate1",y="Successful Rate")+theme(plot.title=element_text(hjust=0.5))+scale_colour_manual(values = c("purple", "magenta", "orange","red", "blue", "green3" ),guide = guide_legend(title="Legend",override.aes = list(linetype = c( "dashed",rep("blank", 2), "solid",rep("blank", 2)),shape = c(NA,rep(16, 2), NA,rep(16, 2) ))))
require(lmtest)
require(strucchange)Loading required package: strucchange
Loading required package: sandwichresettest(crowdfunding$successful_rate~crowdfunding$GiniCoeff+(crowdfunding$GiniCoeff)^2)
RESET test
data: crowdfunding$successful_rate ~ crowdfunding$GiniCoeff + (crowdfunding$GiniCoeff)^2
RESET = 0.62108, df1 = 2, df2 = 46, p-value = 0.5418sctest(log(crowdfunding$successful_rate)~log(crowdfunding$GiniCoeff),type="Chow")
Chow test
data: log(crowdfunding$successful_rate) ~ log(crowdfunding$GiniCoeff)
F = 2.5789, p-value = 0.08679resettest(log(crowdfunding$successful_rate)~log(crowdfunding$pAdDeg))
RESET test
data: log(crowdfunding$successful_rate) ~ log(crowdfunding$pAdDeg)
RESET = 1.7729, df1 = 2, df2 = 46, p-value = 0.1812sctest(crowdfunding$successful_rate~crowdfunding$pAdDeg,type="Chow",point=0.12)
Chow test
data: crowdfunding$successful_rate ~ crowdfunding$pAdDeg
F = 0.55801, p-value = 0.5762plot(crowdfunding$successful_rate~crowdfunding$pAdDeg)
points(lowess(crowdfunding$successful_rate~crowdfunding$pAdDeg),type="l")
b=NA
for (i in 150:170){
a<-sctest(log(crowdfunding$successful_rate)~log(crowdfunding$pAdDeg),type="Chow",point=i/1000)
b[i]=a$p.value
#if (a$p.value<=0.05){print (i/100)}
}
plot(b,type="l")
b[9][1] NAsctest(log(crowdfunding$successful_rate)~log(crowdfunding$pAdDeg),type="Chow",point=0.17)
Chow test
data: log(crowdfunding$successful_rate) ~ log(crowdfunding$pAdDeg)
F = 1.9269, p-value = 0.1572Factors Analysis-Successful Rate|GiniCoeff
ggplot(crowdfunding,aes(x=GiniCoeff,y=successful_rate,main = "Successful rate~GiniCoeff"))+geom_point(group=1,aes(col=Region))+geom_smooth(method = "loess",se=FALSE,lty="dashed",aes(group=2,col="Trendline"))+geom_smooth(method = "lm",aes(group=2,col="Regression"),lty=1,cex=0.5)+ggtitle("Successful Rate-GiniCoeff Plot with loess line and OLS-estimated line")+labs(x="GiniCoeff",y="Successful Rate")+theme(plot.title=element_text(hjust=0.5))
anova(successful_rate2GiniCoeff)Analysis of Variance Table
Response: crowdfunding$successful_rate
Df Sum Sq Mean Sq F value Pr(>F)
crowdfunding$GiniCoeff 1 0.06236 0.062361 9.1537 0.003981 **
Residuals 48 0.32701 0.006813
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1t.test(crowdfunding$successful_rate[crowdfunding$GiniCoeff>mean(crowdfunding$GiniCoeff)],crowdfunding$successful_rate[crowdfunding$GiniCoeff<=mean(crowdfunding$GiniCoeff)])
Welch Two Sample t-test
data: crowdfunding$successful_rate[crowdfunding$GiniCoeff > mean(crowdfunding$GiniCoeff)] and crowdfunding$successful_rate[crowdfunding$GiniCoeff <= mean(crowdfunding$GiniCoeff)]
t = 1.6383, df = 43.111, p-value = 0.1086
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.009375926 0.090607105
sample estimates:
mean of x mean of y
0.3833720 0.3427564 par(mfrow=c(1,2))
boxplot(crowdfunding$successful_rate,crowdfunding$GiniCoeff,names=c("Successful Rate","GiniCoeff"))
boxplot(crowdfunding$successful_rate[crowdfunding$GiniCoeff>mean(crowdfunding$GiniCoeff)],crowdfunding$successful_rate[crowdfunding$GiniCoeff<=mean(crowdfunding$GiniCoeff)],col = c("darkorchid2","dodgerblue"),names=c("Successful%(High GiniCoeff)","Successful%(Low GiniCoeff)"),xlab="Successful rate by GiniCoeff")
plot(crowdfunding$successful_rate,crowdfunding$GiniCoeff,f=1/3 ,pch=19,col="blue",xlab="Successful Rate",ylab="GiniCoeff",main="Successful Rate-GiniCoeff Plot with lowess line")
points(lowess(crowdfunding$successful_rate,crowdfunding$GiniCoeff,f=1/3),pch=4,col="red",type="l")qqplot(crowdfunding$GiniCoeff,crowdfunding$successful_rate,pch=18,main="Q-Q Plot: Successful Rate-GiniCoeff with lowess line",xlab="Gini Coefficient",ylab="Successful Rate")
points(lowess(crowdfunding$GiniCoeff,crowdfunding$successful_rate),type="l",col="red")#qqline(crowdfunding$successful_rate,crowdfunding$GiniCoeff)
#qqnorm(crowdfunding$successful_rate,col="orange",xlab="Successful Rate")
#qqline(crowdfunding$successful_rate,col="red")
par(mfrow=c(1,1))
qqnorm(crowdfunding$GiniCoeff,col="blue",pch=20,xlab="GiniCoeff")
qqline(crowdfunding$GiniCoeff,col="red")
summary(lm(crowdfunding$successful_rate~crowdfunding$GiniCoeff))
Call:
lm(formula = crowdfunding$successful_rate ~ crowdfunding$GiniCoeff)
Residuals:
Min 1Q Median 3Q Max
-0.197648 -0.054730 -0.003818 0.055170 0.201729
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.5417 0.2993 -1.810 0.07655 .
crowdfunding$GiniCoeff 2.0008 0.6613 3.026 0.00398 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.08254 on 48 degrees of freedom
Multiple R-squared: 0.1602, Adjusted R-squared: 0.1427
F-statistic: 9.154 on 1 and 48 DF, p-value: 0.003981ggplot(crowdfunding,aes(x=GiniCoeff,y=successful_rate,main = "Successful rate~GiniCoeff"))+geom_point(aes(group=1,col=Region))+geom_smooth(method = "loess",se=FALSE,aes(group=2,col="Loess Line"),lty="dashed")+geom_smooth(method = "lm",aes(group=2,col="Regression Line"),cex=0.8,lty=1)+ggtitle("Successful Rate-GiniCoeff Plot with Loess line and OLS-estimated line")+labs(x="GiniCoeff",y="Successful Rate")+theme(plot.title=element_text(hjust=0.5))+scale_colour_manual(values = c("purple", "magenta", "orange","red", "blue", "green3" ),guide = guide_legend(title="Legend",override.aes = list(linetype = c( "dashed",rep("blank", 2), "solid",rep("blank", 2)),shape = c(NA,rep(16, 2), NA,rep(16, 2) ))))
Factors Analysis-Successful Rate|Advanced Education]
ggplot(crowdfunding,aes(x=pAdDeg,y=successful_rate))+geom_point(aes(col=Region))+geom_smooth(method = "loess",se=FALSE,lty=2,aes(col="Loess Line"))+geom_smooth(method = "lm",aes(col="Regression Line"),cex=0.8)+ggtitle("Successful Rate-Adanced Education Plot with Loess line and OLS-estimated line")+labs(x="Advanced Education",y="Successful Rate")+theme(plot.title=element_text(hjust=0.5))+scale_colour_manual(values = c("purple", "magenta","orange", "red", "blue", "green3" ),guide = guide_legend(title="Legend",override.aes = list(linetype = c( "dashed",rep("blank", 2), "solid",rep("blank", 2)),shape = c( NA,rep(16, 2), NA,rep(16, 2)))))
ggplot(crowdfunding,aes(x=pAdDeg,y=successful_rate))+geom_point(aes(col=Region))+geom_smooth(method = "loess",se=FALSE,lty=2)+geom_smooth(method = "lm",col="red",cex=0.8)+ggtitle("Successful Rate-Adanced Education Plot")+labs(x="Advanced Education",y="Successful Rate")+theme(plot.title=element_text(hjust=0.5))
plot(lowess(crowdfunding$pAdDeg,crowdfunding$successful_rate))
anova(successful_rate2pAdDeg)Analysis of Variance Table
Response: crowdfunding$successful_rate
Df Sum Sq Mean Sq F value Pr(>F)
crowdfunding$pAdDeg 1 0.05947 0.059469 8.6527 0.005015 **
Residuals 48 0.32990 0.006873
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1par(mfrow=c(1,2))
boxplot(crowdfunding$successful_rate,crowdfunding$pAdDeg,names=c("Successful rate","Adanced Education"))
boxplot(crowdfunding$successful_rate[crowdfunding$pAdDeg>mean(crowdfunding$pAdDeg)],crowdfunding$successful_rate[crowdfunding$pAdDeg<=mean(crowdfunding$pAdDeg)],col = c("darkorchid2","dodgerblue"),names=c("Successful%(High Adanced Education)","Successful%(Low Adanced Education)"),xlab="Successful rate by Adanced Education")
t.test(crowdfunding$successful_rate[crowdfunding$pAdDeg>mean(crowdfunding$pAdDeg)],crowdfunding$successful_rate[crowdfunding$pAdDeg<=mean(crowdfunding$pAdDeg)])
Welch Two Sample t-test
data: crowdfunding$successful_rate[crowdfunding$pAdDeg > mean(crowdfunding$pAdDeg)] and crowdfunding$successful_rate[crowdfunding$pAdDeg <= mean(crowdfunding$pAdDeg)]
t = 3.5483, df = 45.573, p-value = 0.0009119
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
0.03480093 0.12610121
sample estimates:
mean of x mean of y
0.4097258 0.3292747 plot(crowdfunding$successful_rate,crowdfunding$pAdDeg,f=1/3 ,pch=19,col="blue",xlab="Successful Rate",ylab="Adanced Education",main="Successful Rate-Adanced Education Plot with lowess line")
points(lowess(crowdfunding$successful_rate,crowdfunding$pAdDeg,f=1/3),pch=4,col="red",type="l")qqplot(crowdfunding$successful_rate,crowdfunding$pAdDeg,pch=19,col="red",main="Q-Q Plot: Successful Rate-Adanced Education")
qqline(crowdfunding$successful_rate,crowdfunding$pAdDeg)the condition has length > 1 and only the first element will be used#qqnorm(crowdfunding$successful_rate,col="orange",xlab="Successful Rate")
#qqline(crowdfunding$successful_rate,col="red")
par(mfrow=c(1,1))
qqnorm(crowdfunding$pAdDeg,col="blue",pch=20,xlab="Adanced Education")
qqline(crowdfunding$pAdDeg,col="red")
summary(lm(crowdfunding$successful_rate~crowdfunding$pAdDeg))
Call:
lm(formula = crowdfunding$successful_rate ~ crowdfunding$pAdDeg)
Residuals:
Min 1Q Median 3Q Max
-0.22412 -0.06718 0.01152 0.04708 0.15306
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.22570 0.04815 4.688 2.31e-05 ***
crowdfunding$pAdDeg 1.40251 0.47679 2.942 0.00501 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.0829 on 48 degrees of freedom
Multiple R-squared: 0.1527, Adjusted R-squared: 0.1351
F-statistic: 8.653 on 1 and 48 DF, p-value: 0.005015Factors Analysis- Ginicoeff|Advanced Education
require(ggplot2)
ggplot(crowdfunding,aes(x=pAdDeg,y=GiniCoeff))+geom_point(aes(col=Region))+geom_smooth(method = "loess",se=FALSE,lty=2)+geom_smooth(method = "lm",col="red",cex=0.8)+ggtitle("Successful Rate-Adanced Education Plot")+labs(x="Advanced Education",y="GiniCoeff")+theme(plot.title=element_text(hjust=0.5))
plot(lowess(crowdfunding$pAdDeg,crowdfunding$GiniCoeff))
par(mfrow=c(1,2))
boxplot(crowdfunding$GiniCoeff,crowdfunding$pAdDeg,names=c("Successful rate","Adanced Education"))
boxplot(crowdfunding$GiniCoeff[crowdfunding$pAdDeg>mean(crowdfunding$pAdDeg)],crowdfunding$GiniCoeff[crowdfunding$pAdDeg<=mean(crowdfunding$pAdDeg)],col = c("darkorchid2","dodgerblue"),names=c("GiniCoeff(High Adanced Education)","GiniCoeff(Low Adanced Education)"),xlab="GiniCoeff by Adanced Education")
t.test(crowdfunding$GiniCoeff[crowdfunding$pAdDeg>mean(crowdfunding$pAdDeg)],crowdfunding$GiniCoeff[crowdfunding$pAdDeg<=mean(crowdfunding$pAdDeg)])
Welch Two Sample t-test
data: crowdfunding$GiniCoeff[crowdfunding$pAdDeg > mean(crowdfunding$pAdDeg)] and crowdfunding$GiniCoeff[crowdfunding$pAdDeg <= mean(crowdfunding$pAdDeg)]
t = 1.6166, df = 41.849, p-value = 0.1135
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.002035865 0.018423386
sample estimates:
mean of x mean of y
0.4569524 0.4487586 plot(crowdfunding$GiniCoeff,crowdfunding$pAdDeg,f=1/3 ,pch=19,col="blue",xlab="Successful Rate",ylab="Adanced Education",main="GiniCoeff-Adanced Education Plot with lowess line")
points(lowess(crowdfunding$GiniCoeff,crowdfunding$pAdDeg,f=1/3),pch=4,col="red",type="l")qqplot(crowdfunding$GiniCoeff,crowdfunding$pAdDeg,pch=19,col="red",main="Q-Q Plot: GiniCoeff-Adanced Education")
qqline(crowdfunding$GiniCoeff,crowdfunding$pAdDeg)the condition has length > 1 and only the first element will be used#qqnorm(crowdfunding$GiniCoeff,col="orange",xlab="GiniCoeff")
#qqline(crowdfunding$GiniCoeff,col="red")
par(mfrow=c(1,1))
qqnorm(crowdfunding$pAdDeg,col="blue",pch=20,xlab="Adanced Education")
qqline(crowdfunding$pAdDeg,col="red")
require(corpcor)Loading required package: corpcorrequire(ggm)Loading required package: ggm
Loading required package: igraph
Attaching package: 'igraph'
The following objects are masked from 'package:stats':
decompose, spectrum
The following object is masked from 'package:base':
union
Attaching package: 'ggm'
The following object is masked from 'package:igraph':
paxcor=cor(se)#<e7><9b><b8><e5><85><b3><e7><b3><bb><e6><95><b0><e7><9f><a9><e9><98><b5>
xpcor=cor2pcor(xcor)#<e5><81><8f><e7><9b><b8><e5><85><b3><e7><9f><a9><e9><98><b5>
xpcor [,1] [,2] [,3]
[1,] 1.0000000 0.1683237 0.3227919
[2,] 0.1683237 1.0000000 0.3102785
[3,] 0.3227919 0.3102785 1.0000000xcor GiniCoeff pAdDeg successful_rate
GiniCoeff 1.0000000 0.2983904 0.4001989
pAdDeg 0.2983904 1.0000000 0.3908097
successful_rate 0.4001989 0.3908097 1.0000000require(psych)Loading required package: psych
Attaching package: 'psych'
The following object is masked from 'package:car':
logit
The following objects are masked from 'package:ggplot2':
%+%, alphau<-colnames(xcor)
u[1] "GiniCoeff" "pAdDeg" "successful_rate"pcortest_dev<-function(x_set,y,dataset){
require(psych)
require(ggm)
for (nv in length(x_set) ){
data_subset<-dataset
data_subset[x_set[nv]]<-NULL
x_subset=x_set
x_subset=x_subset[-nv]
jsbl <- c(x_subset,y) #<e8><a6><81><e8><ae><a1><e7><ae><97><e7><9a><84><e7><9b><b8><e5><85><b3><e7><b3><bb><e6><95><b0><e7><9a><84><e5><8f><98><e9><87><8f><e4><b8><8b><e6><a0><87>
tjbl <- c(x_set[nv]) #<e6><9d><a1><e4><bb><b6>(<e6><8e><a7><e5><88><b6>)<e5><8f><98><e9><87><8f><e7><9a><84><e4><b8><8b><e6><a0><87><ef><bc><8c><e5><8d><b3><e8><a6><81><e6><8e><92><e9><99><a4><e5><bd><b1><e5><93><8d><e7><9a><84><e5><8f><98><e9><87><8f><e7><9a><84><e4><b8><8b><e6><a0><87>
r<-pcor(c(jsbl,tjbl),cor(dataset))
q <- length(tjbl)
n <- dim(dataset)[1]
print(x_set[nv])
pcor_test<- pcor.test(r,q,n)
print(pcor_test)
}
return(pcor_test)
}
pcortest_dev(c("GiniCoeff","pAdDeg"),c("successful_rate"),se)[1] "pAdDeg"
$tval
[1] 2.338108
$df
[1] 47
$pvalue
[1] 0.02368857
$tval
[1] 2.338108
$df
[1] 47
$pvalue
[1] 0.02368857a<-1
a1_temp=lm(crowdfunding$successful_rate~crowdfunding$GiniCoeff)
b1_temp=lm(crowdfunding$pAdDeg~crowdfunding$GiniCoeff)
cor.test(a1_temp$residuals,b1_temp$residuals)
Pearson's product-moment correlation
data: a1_temp$residuals and b1_temp$residuals
t = 2.2613, df = 48, p-value = 0.02831
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
0.03494915 0.54183081
sample estimates:
cor
0.3102785 a2_temp=lm(crowdfunding$successful_rate~crowdfunding$pAdDeg)
b2_temp=lm(crowdfunding$GiniCoeff~crowdfunding$pAdDeg)
se_s[1]jsbl <- c("pAdDeg","successful_rate") #<e8><a6><81><e8><ae><a1><e7><ae><97><e7><9a><84><e7><9b><b8><e5><85><b3><e7><b3><bb><e6><95><b0><e7><9a><84><e5><8f><98><e9><87><8f><e4><b8><8b><e6><a0><87>
tjbl <- c("GiniCoeff") #<e6><9d><a1><e4><bb><b6>(<e6><8e><a7><e5><88><b6>)<e5><8f><98><e9><87><8f><e7><9a><84><e4><b8><8b><e6><a0><87><ef><bc><8c><e5><8d><b3><e8><a6><81><e6><8e><92><e9><99><a4><e5><bd><b1><e5><93><8d><e7><9a><84><e5><8f><98><e9><87><8f><e7><9a><84><e4><b8><8b><e6><a0><87>
jsbl <- c("GiniCoeff","successful_rate") #<e8><a6><81><e8><ae><a1><e7><ae><97><e7><9a><84><e7><9b><b8><e5><85><b3><e7><b3><bb><e6><95><b0><e7><9a><84><e5><8f><98><e9><87><8f><e4><b8><8b><e6><a0><87>
tjbl <- c("pAdDeg") #<e6><9d><a1><e4><bb><b6>(<e6><8e><a7><e5><88><b6>)<e5><8f><98><e9><87><8f><e7><9a><84><e4><b8><8b><e6><a0><87><ef><bc><8c><e5><8d><b3><e8><a6><81><e6><8e><92><e9><99><a4><e5><bd><b1><e5><93><8d><e7><9a><84><e5><8f><98><e9><87><8f><e7><9a><84><e4><b8><8b><e6><a0><87>
r<-pcor(c(jsbl,tjbl),cor(se))
q <- length(tjbl) #<e8><ae><a1><e7><ae><97><e8><a6><81><e6><8e><a7><e5><88><b6><e7><9a><84><e5><8f><98><e9><87><8f><e6><95><b0>
n <- dim(se)[1] #<e8><ae><a1><e7><ae><97><e6><a0><b7><e6><9c><ac><e9><87><8f>
pcor_test.GiniCoeff <- pcor.test(r,q,n) #<e5><81><8f><e7><9b><b8><e5><85><b3><e7><b3><bb><e6><95><b0><e6><98><be><e8><91><97><e6><80><a7><e6><a3><80><e9><aa><8c><e7><bb><93><e6><9e><9c><ef><bc><88><e8><be><93><e5><87><ba><e5><ba><8f><e5><8f><b7>3<ef><bc><89>
r.GiniCoeff=r
pcor_test.GiniCoeff$tval
[1] 2.338108
$df
[1] 47
$pvalue
[1] 0.02368857pcor_test.pAdDeg <- pcor.test(r,q,n)
r.pAdDeg=r
pcor_test.pAdDeg$tval
[1] 2.338108
$df
[1] 47
$pvalue
[1] 0.02368857corr.test(se,use="pairwise")Call:corr.test(x = se, use = "pairwise")
Correlation matrix
GiniCoeff pAdDeg successful_rate
GiniCoeff 1.0 0.30 0.40
pAdDeg 0.3 1.00 0.39
successful_rate 0.4 0.39 1.00
Sample Size
[1] 50
Probability values (Entries above the diagonal are adjusted for multiple tests.)
GiniCoeff pAdDeg successful_rate
GiniCoeff 0.00 0.04 0.01
pAdDeg 0.04 0.00 0.01
successful_rate 0.00 0.01 0.00
To see confidence intervals of the correlations, print with the short=FALSE optionsummary(lm(crowdfunding$GiniCoeff~crowdfunding$pAdDeg))
Call:
lm(formula = crowdfunding$GiniCoeff ~ crowdfunding$pAdDeg)
Residuals:
Min 1Q Median 3Q Max
-0.031714 -0.014215 0.001214 0.013231 0.037791
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.431222 0.009986 43.184 <2e-16 ***
crowdfunding$pAdDeg 0.214191 0.098889 2.166 0.0353 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.01719 on 48 degrees of freedom
Multiple R-squared: 0.08904, Adjusted R-squared: 0.07006
F-statistic: 4.691 on 1 and 48 DF, p-value: 0.03531cor.test(crowdfunding$GiniCoeff,crowdfunding$pAdDeg)
Pearson's product-moment correlation
data: crowdfunding$GiniCoeff and crowdfunding$pAdDeg
t = 2.166, df = 48, p-value = 0.03531
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
0.02185813 0.53250982
sample estimates:
cor
0.2983904 summary(lm(log(crowdfunding$GiniCoeff)~log(crowdfunding$pAdDeg)))
Call:
lm(formula = log(crowdfunding$GiniCoeff) ~ log(crowdfunding$pAdDeg))
Residuals:
Min 1Q Median 3Q Max
-0.073531 -0.032117 0.002537 0.028966 0.082244
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.69086 0.05426 -12.732 <2e-16 ***
log(crowdfunding$pAdDeg) 0.04401 0.02295 1.918 0.0611 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.03835 on 48 degrees of freedom
Multiple R-squared: 0.07116, Adjusted R-squared: 0.05181
F-statistic: 3.677 on 1 and 48 DF, p-value: 0.06112cor.test(log(crowdfunding$GiniCoeff),log(crowdfunding$pAdDeg))
Pearson's product-moment correlation
data: log(crowdfunding$GiniCoeff) and log(crowdfunding$pAdDeg)
t = 1.9176, df = 48, p-value = 0.06112
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
-0.01251929 0.50742841
sample estimates:
cor
0.266758 qqplot(log(crowdfunding$successful_rate),log(crowdfunding$GiniCoeff))
qqplot(log(crowdfunding$successful_rate),log(crowdfunding$pAdDeg))
require(gvlma)Loading required package: gvlmasummary(gvlma(lm(log(crowdfunding$successful_rate)~log(crowdfunding$pAdDeg))))
Call:
lm(formula = log(crowdfunding$successful_rate) ~ log(crowdfunding$pAdDeg))
Residuals:
Min 1Q Median 3Q Max
-0.99585 -0.16975 0.06631 0.15860 0.35118
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.02673 0.39131 -0.068 0.9458
log(crowdfunding$pAdDeg) 0.43484 0.16552 2.627 0.0115 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2766 on 48 degrees of freedom
Multiple R-squared: 0.1257, Adjusted R-squared: 0.1075
F-statistic: 6.902 on 1 and 48 DF, p-value: 0.01152
ASSESSMENT OF THE LINEAR MODEL ASSUMPTIONS
USING THE GLOBAL TEST ON 4 DEGREES-OF-FREEDOM:
Level of Significance = 0.05
Call:
gvlma(x = lm(log(crowdfunding$successful_rate) ~ log(crowdfunding$pAdDeg)))
Value p-value Decision
Global Stat 63.40303 5.582e-13 Assumptions NOT satisfied!
Skewness 23.67087 1.143e-06 Assumptions NOT satisfied!
Kurtosis 35.36041 2.740e-09 Assumptions NOT satisfied!
Link Function 0.01624 8.986e-01 Assumptions acceptable.
Heteroscedasticity 4.35552 3.689e-02 Assumptions NOT satisfied!summary(gvlma(lm(crowdfunding$successful_rate~crowdfunding$pAdDeg)))
Call:
lm(formula = crowdfunding$successful_rate ~ crowdfunding$pAdDeg)
Residuals:
Min 1Q Median 3Q Max
-0.22412 -0.06718 0.01152 0.04708 0.15306
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.22570 0.04815 4.688 2.31e-05 ***
crowdfunding$pAdDeg 1.40251 0.47679 2.942 0.00501 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.0829 on 48 degrees of freedom
Multiple R-squared: 0.1527, Adjusted R-squared: 0.1351
F-statistic: 8.653 on 1 and 48 DF, p-value: 0.005015
ASSESSMENT OF THE LINEAR MODEL ASSUMPTIONS
USING THE GLOBAL TEST ON 4 DEGREES-OF-FREEDOM:
Level of Significance = 0.05
Call:
gvlma(x = lm(crowdfunding$successful_rate ~ crowdfunding$pAdDeg))
Value p-value Decision
Global Stat 4.3514 0.3605 Assumptions acceptable.
Skewness 1.8403 0.1749 Assumptions acceptable.
Kurtosis 0.1144 0.7351 Assumptions acceptable.
Link Function 0.6963 0.4040 Assumptions acceptable.
Heteroscedasticity 1.7004 0.1922 Assumptions acceptable.summary(gvlma(lm(log(crowdfunding$successful_rate)~log(crowdfunding$GiniCoeff))))
Call:
lm(formula = log(crowdfunding$successful_rate) ~ log(crowdfunding$GiniCoeff))
Residuals:
Min 1Q Median 3Q Max
-0.89272 -0.13415 0.03076 0.16062 0.50236
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.3738 0.7782 1.765 0.08385 .
log(crowdfunding$GiniCoeff) 3.0507 0.9784 3.118 0.00307 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2697 on 48 degrees of freedom
Multiple R-squared: 0.1684, Adjusted R-squared: 0.1511
F-statistic: 9.722 on 1 and 48 DF, p-value: 0.003074
ASSESSMENT OF THE LINEAR MODEL ASSUMPTIONS
USING THE GLOBAL TEST ON 4 DEGREES-OF-FREEDOM:
Level of Significance = 0.05
Call:
gvlma(x = lm(log(crowdfunding$successful_rate) ~ log(crowdfunding$GiniCoeff)))
Value p-value Decision
Global Stat 28.9877 7.862e-06 Assumptions NOT satisfied!
Skewness 11.4306 7.225e-04 Assumptions NOT satisfied!
Kurtosis 12.4173 4.254e-04 Assumptions NOT satisfied!
Link Function 0.5972 4.396e-01 Assumptions acceptable.
Heteroscedasticity 4.5427 3.306e-02 Assumptions NOT satisfied!summary(gvlma(lm(crowdfunding$successful_rate~crowdfunding$GiniCoeff)))
Call:
lm(formula = crowdfunding$successful_rate ~ crowdfunding$GiniCoeff)
Residuals:
Min 1Q Median 3Q Max
-0.197648 -0.054730 -0.003818 0.055170 0.201729
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.5417 0.2993 -1.810 0.07655 .
crowdfunding$GiniCoeff 2.0008 0.6613 3.026 0.00398 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.08254 on 48 degrees of freedom
Multiple R-squared: 0.1602, Adjusted R-squared: 0.1427
F-statistic: 9.154 on 1 and 48 DF, p-value: 0.003981
ASSESSMENT OF THE LINEAR MODEL ASSUMPTIONS
USING THE GLOBAL TEST ON 4 DEGREES-OF-FREEDOM:
Level of Significance = 0.05
Call:
gvlma(x = lm(crowdfunding$successful_rate ~ crowdfunding$GiniCoeff))
Value p-value Decision
Global Stat 2.77666 0.5959 Assumptions acceptable.
Skewness 0.03904 0.8434 Assumptions acceptable.
Kurtosis 0.03367 0.8544 Assumptions acceptable.
Link Function 0.11971 0.7294 Assumptions acceptable.
Heteroscedasticity 2.58424 0.1079 Assumptions acceptable.summary(lm(crowdfunding$successful_rate~crowdfunding$GiniCoeff))
Call:
lm(formula = crowdfunding$successful_rate ~ crowdfunding$GiniCoeff)
Residuals:
Min 1Q Median 3Q Max
-0.197648 -0.054730 -0.003818 0.055170 0.201729
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.5417 0.2993 -1.810 0.07655 .
crowdfunding$GiniCoeff 2.0008 0.6613 3.026 0.00398 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.08254 on 48 degrees of freedom
Multiple R-squared: 0.1602, Adjusted R-squared: 0.1427
F-statistic: 9.154 on 1 and 48 DF, p-value: 0.003981par(mfrow=c(2,2))
plot(lm(log(crowdfunding$successful_rate)~log(crowdfunding$pAdDeg)))
plot(lm(crowdfunding$successful_rate~crowdfunding$pAdDeg))
plot(lm(crowdfunding$successful_rate~crowdfunding$GiniCoeff))
SR2Gini<-lm(crowdfunding$successful_rate~crowdfunding$GiniCoeff)
cor.test(crowdfunding$successful_rate,crowdfunding$GiniCoeff,method = "spearman")Cannot compute exact p-value with ties
Spearman's rank correlation rho
data: crowdfunding$successful_rate and crowdfunding$GiniCoeff
S = 13615, p-value = 0.01378
alternative hypothesis: true rho is not equal to 0
sample estimates:
rho
0.3462269 summary(lm(SR2Gini$residuals~crowdfunding$pAdDeg))
Call:
lm(formula = SR2Gini$residuals ~ crowdfunding$pAdDeg)
Residuals:
Min 1Q Median 3Q Max
-0.187967 -0.048021 -0.005676 0.052160 0.167582
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.09539 0.04578 -2.083 0.0426 *
crowdfunding$pAdDeg 0.97396 0.45341 2.148 0.0368 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.07884 on 48 degrees of freedom
Multiple R-squared: 0.0877, Adjusted R-squared: 0.06869
F-statistic: 4.614 on 1 and 48 DF, p-value: 0.03678cor.test(crowdfunding$successful_rate,crowdfunding$pAdDeg,method = "spearman")Cannot compute exact p-value with ties
Spearman's rank correlation rho
data: crowdfunding$successful_rate and crowdfunding$pAdDeg
S = 12634, p-value = 0.004719
alternative hypothesis: true rho is not equal to 0
sample estimates:
rho
0.3933076 summary(gvlma(lm(SR2Gini$residuals~crowdfunding$pAdDeg)))
Call:
lm(formula = SR2Gini$residuals ~ crowdfunding$pAdDeg)
Residuals:
Min 1Q Median 3Q Max
-0.187967 -0.048021 -0.005676 0.052160 0.167582
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.09539 0.04578 -2.083 0.0426 *
crowdfunding$pAdDeg 0.97396 0.45341 2.148 0.0368 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.07884 on 48 degrees of freedom
Multiple R-squared: 0.0877, Adjusted R-squared: 0.06869
F-statistic: 4.614 on 1 and 48 DF, p-value: 0.03678
ASSESSMENT OF THE LINEAR MODEL ASSUMPTIONS
USING THE GLOBAL TEST ON 4 DEGREES-OF-FREEDOM:
Level of Significance = 0.05
Call:
gvlma(x = lm(SR2Gini$residuals ~ crowdfunding$pAdDeg))
Value p-value Decision
Global Stat 2.96167 0.5643 Assumptions acceptable.
Skewness 0.38039 0.5374 Assumptions acceptable.
Kurtosis 0.01984 0.8880 Assumptions acceptable.
Link Function 1.96967 0.1605 Assumptions acceptable.
Heteroscedasticity 0.59177 0.4417 Assumptions acceptable.SR2ad<-lm(crowdfunding$successful_rate~crowdfunding$pAdDeg)
cor.test(crowdfunding$successful_rate,crowdfunding$pAdDeg)
Pearson's product-moment correlation
data: crowdfunding$successful_rate and crowdfunding$pAdDeg
t = 2.9415, df = 48, p-value = 0.005015
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
0.1261889 0.6035073
sample estimates:
cor
0.3908097 summary(lm(SR2ad$residuals~crowdfunding$GiniCoeff))
Call:
lm(formula = SR2ad$residuals ~ crowdfunding$GiniCoeff)
Residuals:
Min 1Q Median 3Q Max
-0.195484 -0.051186 0.004758 0.054168 0.147777
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.6411 0.2860 -2.242 0.0296 *
crowdfunding$GiniCoeff 1.4178 0.6319 2.244 0.0295 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.07887 on 48 degrees of freedom
Multiple R-squared: 0.09492, Adjusted R-squared: 0.07606
F-statistic: 5.034 on 1 and 48 DF, p-value: 0.02951summary(gvlma(lm(SR2ad$residuals~crowdfunding$GiniCoeff)))
Call:
lm(formula = SR2ad$residuals ~ crowdfunding$GiniCoeff)
Residuals:
Min 1Q Median 3Q Max
-0.195484 -0.051186 0.004758 0.054168 0.147777
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.6411 0.2860 -2.242 0.0296 *
crowdfunding$GiniCoeff 1.4178 0.6319 2.244 0.0295 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.07887 on 48 degrees of freedom
Multiple R-squared: 0.09492, Adjusted R-squared: 0.07606
F-statistic: 5.034 on 1 and 48 DF, p-value: 0.02951
ASSESSMENT OF THE LINEAR MODEL ASSUMPTIONS
USING THE GLOBAL TEST ON 4 DEGREES-OF-FREEDOM:
Level of Significance = 0.05
Call:
gvlma(x = lm(SR2ad$residuals ~ crowdfunding$GiniCoeff))
Value p-value Decision
Global Stat 2.34938 0.6718 Assumptions acceptable.
Skewness 1.05702 0.3039 Assumptions acceptable.
Kurtosis 0.04151 0.8386 Assumptions acceptable.
Link Function 0.83145 0.3619 Assumptions acceptable.
Heteroscedasticity 0.41941 0.5172 Assumptions acceptable.summary(gvlma(lm(crowdfunding$successful_rate~crowdfunding$GiniCoeff+crowdfunding$pAdDeg)))
Call:
lm(formula = crowdfunding$successful_rate ~ crowdfunding$GiniCoeff +
crowdfunding$pAdDeg)
Residuals:
Min 1Q Median 3Q Max
-0.195999 -0.049936 -0.001397 0.053631 0.160600
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.4454 0.2907 -1.532 0.1322
crowdfunding$GiniCoeff 1.5563 0.6656 2.338 0.0237 *
crowdfunding$pAdDeg 1.0692 0.4778 2.238 0.0300 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.0793 on 47 degrees of freedom
Multiple R-squared: 0.241, Adjusted R-squared: 0.2087
F-statistic: 7.462 on 2 and 47 DF, p-value: 0.001533
ASSESSMENT OF THE LINEAR MODEL ASSUMPTIONS
USING THE GLOBAL TEST ON 4 DEGREES-OF-FREEDOM:
Level of Significance = 0.05
Call:
gvlma(x = lm(crowdfunding$successful_rate ~ crowdfunding$GiniCoeff + crowdfunding$pAdDeg))
Value p-value Decision
Global Stat 4.746089 0.31436 Assumptions acceptable.
Skewness 0.710171 0.39939 Assumptions acceptable.
Kurtosis 0.002151 0.96300 Assumptions acceptable.
Link Function 3.157228 0.07559 Assumptions acceptable.
Heteroscedasticity 0.876539 0.34915 Assumptions acceptable.require(car)
#SR2Gini---------
qqPlot(SR2Gini,labels=row.names(crowdfunding),id.method="identify",simulate=TRUE,main="Q-Q Plot")
influencePlot(SR2Gini,labels=row.names(crowdfunding),id.method="identify",main="Influence Plot", sub="Circle size if proportional to Cook's distance") #SR2all----------
SR2GiniAdedc<-lm(successful_rate~GiniCoeff+pAdDeg,data=crowdfunding)
qqPlot(SR2GiniAdedc,labels=row.names(crowdfunding),id.method="identify",simulate=TRUE,main="Q-Q Plot")
influencePlot(SR2GiniAdedc,labels=row.names(crowdfunding),id.method="identify",main="Influence Plot", sub="Circle size if proportional to Cook's distance") 
#
anova(SR2GiniAdedc)Analysis of Variance Table
Response: successful_rate
Df Sum Sq Mean Sq F value Pr(>F)
GiniCoeff 1 0.062361 0.062361 9.9178 0.002844 **
pAdDeg 1 0.031482 0.031482 5.0068 0.030026 *
Residuals 47 0.295525 0.006288
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(gvlma(lm(successful_rate~NPov000s,data=crowdfunding)))
Call:
lm(formula = successful_rate ~ NPov000s, data = crowdfunding)
Residuals:
Min 1Q Median 3Q Max
-0.230673 -0.041789 -0.001671 0.045678 0.193621
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.541e-01 1.642e-02 21.560 <2e-16 ***
NPov000s 9.537e-06 1.113e-05 0.857 0.396
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.08938 on 48 degrees of freedom
Multiple R-squared: 0.01507, Adjusted R-squared: -0.005451
F-statistic: 0.7343 on 1 and 48 DF, p-value: 0.3957
ASSESSMENT OF THE LINEAR MODEL ASSUMPTIONS
USING THE GLOBAL TEST ON 4 DEGREES-OF-FREEDOM:
Level of Significance = 0.05
Call:
gvlma(x = lm(successful_rate ~ NPov000s, data = crowdfunding))
Value p-value Decision
Global Stat 6.8904 0.14179 Assumptions acceptable.
Skewness 0.5449 0.46041 Assumptions acceptable.
Kurtosis 0.5976 0.43948 Assumptions acceptable.
Link Function 0.9871 0.32046 Assumptions acceptable.
Heteroscedasticity 4.7608 0.02911 Assumptions NOT satisfied!SR2ginieduclevel<-lm(successful_rate~pAdDeg+PovRate1,data=crowdfunding)
summary(gvlma(SR2ginieduclevel))
Call:
lm(formula = successful_rate ~ pAdDeg + PovRate1, data = crowdfunding)
Residuals:
Min 1Q Median 3Q Max
-0.180982 -0.056105 -0.000637 0.054745 0.142832
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.06244 0.09334 -0.669 0.50678
pAdDeg 2.24363 0.49262 4.554 3.73e-05 ***
PovRate1 1.39012 0.39874 3.486 0.00107 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.07468 on 47 degrees of freedom
Multiple R-squared: 0.3268, Adjusted R-squared: 0.2982
F-statistic: 11.41 on 2 and 47 DF, p-value: 9.144e-05
ASSESSMENT OF THE LINEAR MODEL ASSUMPTIONS
USING THE GLOBAL TEST ON 4 DEGREES-OF-FREEDOM:
Level of Significance = 0.05
Call:
gvlma(x = SR2ginieduclevel)
Value p-value Decision
Global Stat 2.4121 0.6604 Assumptions acceptable.
Skewness 0.2126 0.6448 Assumptions acceptable.
Kurtosis 0.3593 0.5489 Assumptions acceptable.
Link Function 1.6219 0.2028 Assumptions acceptable.
Heteroscedasticity 0.2184 0.6403 Assumptions acceptable.logSR2ginieduclevel<-lm(log(successful_rate)~log(pAdDeg)+log(PovRate1),data=crowdfunding)
summary(gvlma(logSR2ginieduclevel))
Call:
lm(formula = log(successful_rate) ~ log(pAdDeg) + log(PovRate1),
data = crowdfunding)
Residuals:
Min 1Q Median 3Q Max
-0.85136 -0.12078 0.03723 0.16421 0.32414
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.1251 0.6603 3.218 0.002339 **
log(pAdDeg) 0.7485 0.1675 4.468 4.96e-05 ***
log(PovRate1) 0.7320 0.1915 3.823 0.000387 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2441 on 47 degrees of freedom
Multiple R-squared: 0.3331, Adjusted R-squared: 0.3047
F-statistic: 11.74 on 2 and 47 DF, p-value: 7.329e-05
ASSESSMENT OF THE LINEAR MODEL ASSUMPTIONS
USING THE GLOBAL TEST ON 4 DEGREES-OF-FREEDOM:
Level of Significance = 0.05
Call:
gvlma(x = logSR2ginieduclevel)
Value p-value Decision
Global Stat 26.409 2.616e-05 Assumptions NOT satisfied!
Skewness 13.302 2.652e-04 Assumptions NOT satisfied!
Kurtosis 10.646 1.103e-03 Assumptions NOT satisfied!
Link Function 1.454 2.278e-01 Assumptions acceptable.
Heteroscedasticity 1.007 3.155e-01 Assumptions acceptable.test1<-lm(pAdDeg~PovRate1,data=crowdfunding)
test2<-lm(PovRate1~pAdDeg,data=crowdfunding)
plot(SR2ginieduclevel)
anova(SR2ginieduclevel)Analysis of Variance Table
Response: successful_rate
Df Sum Sq Mean Sq F value Pr(>F)
pAdDeg 1 0.059469 0.059469 10.663 0.002043 **
PovRate1 1 0.067783 0.067783 12.154 0.001073 **
Residuals 47 0.262116 0.005577
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#plot(crowdfunding$education_leverage,crowdfunding$successful_rate)
#boxplot(crowdfunding$successful_rate[crowdfunding$education_leverage==1],crowdfunding$successful_rate[crowdfunding$education_leverage==6])
#vif:Variance Inflation Factor----------
vif(SR2GiniAdedc)GiniCoeff pAdDeg
1.097739 1.097739 vif(SR2ginieduclevel) pAdDeg PovRate1
1.315553 1.315553 sqrt(vif(SR2GiniAdedc))>2GiniCoeff pAdDeg
FALSE FALSE sqrt(vif(SR2ginieduclevel))>2 pAdDeg PovRate1
FALSE FALSE #PCA---------
prcomp(~pAdDeg+PovRate1,data=crowdfunding)Standard deviations:
[1] 0.03444520 0.01929402
Rotation:
PC1 PC2
pAdDeg -0.5482532 -0.8363124
PovRate1 0.8363124 -0.5482532require(moments)
kurtosis(crowdfunding$successful_rate)[1] 3.630147skewness(crowdfunding$successful_rate)[1] -0.3574304summary(gvlma(lm(crowdfunding$successful_rate~test1$residuals)))
Call:
lm(formula = crowdfunding$successful_rate ~ test1$residuals)
Residuals:
Min 1Q Median 3Q Max
-0.193509 -0.054867 0.003501 0.055271 0.137047
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.36306 0.01068 33.999 < 2e-16 ***
test1$residuals 2.24363 0.49810 4.504 4.26e-05 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.07551 on 48 degrees of freedom
Multiple R-squared: 0.2971, Adjusted R-squared: 0.2825
F-statistic: 20.29 on 1 and 48 DF, p-value: 4.259e-05
ASSESSMENT OF THE LINEAR MODEL ASSUMPTIONS
USING THE GLOBAL TEST ON 4 DEGREES-OF-FREEDOM:
Level of Significance = 0.05
Call:
gvlma(x = lm(crowdfunding$successful_rate ~ test1$residuals))
Value p-value Decision
Global Stat 4.3345 0.36263 Assumptions acceptable.
Skewness 0.8744 0.34974 Assumptions acceptable.
Kurtosis 0.1683 0.68162 Assumptions acceptable.
Link Function 3.0237 0.08206 Assumptions acceptable.
Heteroscedasticity 0.2681 0.60463 Assumptions acceptable.summary(gvlma(lm(crowdfunding$successful_rate~test2$residuals)))
Call:
lm(formula = crowdfunding$successful_rate ~ test2$residuals)
Residuals:
Min 1Q Median 3Q Max
-0.194923 -0.055056 -0.003674 0.055637 0.192004
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.36306 0.01158 31.365 < 2e-16 ***
test2$residuals 1.39012 0.43704 3.181 0.00257 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.08185 on 48 degrees of freedom
Multiple R-squared: 0.1741, Adjusted R-squared: 0.1569
F-statistic: 10.12 on 1 and 48 DF, p-value: 0.002574
ASSESSMENT OF THE LINEAR MODEL ASSUMPTIONS
USING THE GLOBAL TEST ON 4 DEGREES-OF-FREEDOM:
Level of Significance = 0.05
Call:
gvlma(x = lm(crowdfunding$successful_rate ~ test2$residuals))
Value p-value Decision
Global Stat 5.248197 0.26277 Assumptions acceptable.
Skewness 0.002307 0.96169 Assumptions acceptable.
Kurtosis 0.017605 0.89444 Assumptions acceptable.
Link Function 3.162908 0.07533 Assumptions acceptable.
Heteroscedasticity 2.065378 0.15068 Assumptions acceptable.---
title: "Assignment"
author: "sn0wfree"
date: "12/16/2016"
output:
  html_notebook:
    toc: yes
  html_document:
    toc: yes
  pdf_document:
    toc: yes
---


## Import Data
```{r import date}
crowdfunding<-read.csv( "forqrm.csv" ,header=1)
crowdfunding_region<-read.csv( "forqrm.csv" ,header=1)
head(crowdfunding)
library(lmtest)

rownames(crowdfunding)<-crowdfunding$State
se<-crowdfunding[c("GiniCoeff","pAdDeg","successful_rate")]
se
#cor
```

## Find Topics
### 1.Geography information

1. Geography information:found the significally different by state/by region
  + Amount
  + successfull rate
  within graphy/plot
  
### 2.Factors analysis:
  
2. factors:studying the relationship between Successful Rate and other factors:
  + Higher Eduction:pAdDeg;
  + Ginicoeff
  # average_pledged_amount_of_Grand.Total#
```{r pie}
pie(table(crowdfunding_region$Region))

```


### 3.Total regression
```{r regression}

successful_rate2GiniCoeff<-lm(crowdfunding$successful_rate~crowdfunding$GiniCoeff)#significant:0.00398
summary(successful_rate2GiniCoeff)

successful_rate2NPov000s<-lm(crowdfunding$successful_rate~crowdfunding$NPov000s)#0.231
summary(successful_rate2NPov000s)

summary(lm(crowdfunding$successful_rate~crowdfunding$pHigh))#bad:0.2320 
summary(lm(crowdfunding$successful_rate~crowdfunding$pBatDeg))#low:0.05511

successful_rate2pAdDeg<-lm(crowdfunding$successful_rate~crowdfunding$pAdDeg)#significant:0.00501
summary(successful_rate2pAdDeg)

#supplement regression
summary(lm(crowdfunding$GiniCoeff~crowdfunding$pAdDeg))#significant:0.0353


summary(lm(crowdfunding$count_of_Grand.Total~crowdfunding$Pop2010))#significant:0.0353

#summary(lm(successful_rate2PovRate1$residuals~crowdfunding$pAdDeg))#0.0003881
#summary(lm(successful_rate2PovRate1$residuals~crowdfunding$GiniCoeff))#0.0264

summary(lm(successful_rate2GiniCoeff$residuals~crowdfunding$pAdDeg))#residuals ~ ADdeg:0.0368
#summary(lm(successful_rate2GiniCoeff$residuals~crowdfunding$PovRate1))#residuals ~ PovRate1:0.771

#summary(lm(successful_rate2pAdDeg$residuals~crowdfunding$PovRate1))#0.004505
summary(lm(successful_rate2pAdDeg$residuals~crowdfunding$GiniCoeff))#0.0295


#summary(lm(log(crowdfunding$average_pledged_amount_of_Grand.Total)~crowdfunding$GiniCoeff))#0.02357


```

## Geography information



### Geography information-preprocess:kmean
```{r kmeans}
hc<-hclust(dist(crowdfunding),method = "ward.D", members = NULL)
plclust(hc)
rect.hclust(hc,k=2)


heatmap(as.matrix(dist(crowdfunding,method= 'euclidean')),labRow = F, labCol = F)
result<-cutree(hc,k=2)
result.category<-as.data.frame(result)
colnames(result.category)<-c("MDS_Category")
result.category
pie(result)
barplot(result,col =result )
#table(result)
#summary(result)
plot(result,type = "p",col=result,xlab="State",xaxt="n",ylab="MDS_Category")


library(ggplot2)
mds2 <- -cmdscale(dist(crowdfunding))
plot(mds2, type="n", axes=FALSE, ann=FALSE)
text(mds2, labels=rownames(mds2), xpd = NA)

mds<-cmdscale(dist(crowdfunding),k=2,eig=T)
x = mds$points[,1]
y = mds$points[,2]
p=ggplot(data.frame(x,y),aes(x,y))
p+geom_point(size=5 , alpha=0.8 , aes(colour=factor(result) ))


  
```



```{r regroup}
#regroup-------------------------------
#crowdfunding.backup<-crowdfunding

result.category["State"]<-row.names(result.category)

#crowdfunding<-crowdfunding[c("State","successful_rate","Region","count_of_Grand.Total","GiniCoeff","pAdDeg","pHigh","pBatDeg","Pop2010")]
#crowdfunding$Pop2010<-log(crowdfunding$Pop2010)
#crowdfunding$count_of_Grand.Total<-log(crowdfunding$count_of_Grand.Total)
crowdfunding<-merge(crowdfunding,result.category,all.x=TRUE)
#row.names(crowdfunding)<-crowdfunding$State

#GiniCoeff-------
par(mfrow=c(1,2))
plot(crowdfunding$successful_rate[crowdfunding$MDS_Category==1]~crowdfunding$GiniCoeff[crowdfunding$MDS_Category==1])
points(lowess(crowdfunding$successful_rate[crowdfunding$MDS_Category==1]~crowdfunding$GiniCoeff[crowdfunding$MDS_Category==1]),type="l",col="red")

plot(crowdfunding$successful_rate[crowdfunding$MDS_Category==2]~crowdfunding$GiniCoeff[crowdfunding$MDS_Category==2])
points(lowess(crowdfunding$successful_rate[crowdfunding$MDS_Category==2]~crowdfunding$GiniCoeff[crowdfunding$MDS_Category==2]),type="l",col="red")

summary(lm(crowdfunding$successful_rate[crowdfunding$MDS_Category==1]~crowdfunding$GiniCoeff[crowdfunding$MDS_Category==1]))
summary(lm(crowdfunding$successful_rate[crowdfunding$MDS_Category==2]~crowdfunding$GiniCoeff[crowdfunding$MDS_Category==2]))

#pAdDeg-------
plot(crowdfunding$successful_rate[crowdfunding$MDS_Category==1]~crowdfunding$pAdDeg[crowdfunding$MDS_Category==1])
points(lowess(crowdfunding$successful_rate[crowdfunding$MDS_Category==1]~crowdfunding$pAdDeg[crowdfunding$MDS_Category==1]),type="l",col="red")
plot(crowdfunding$successful_rate[crowdfunding$MDS_Category==2]~crowdfunding$pAdDeg[crowdfunding$MDS_Category==2])
points(lowess(crowdfunding$successful_rate[crowdfunding$MDS_Category==2]~crowdfunding$pAdDeg[crowdfunding$MDS_Category==2]),type="l",col="red")

summary(lm(crowdfunding$successful_rate[crowdfunding$MDS_Category==1]~crowdfunding$pAdDeg[crowdfunding$MDS_Category==1]))
summary(lm(crowdfunding$successful_rate[crowdfunding$MDS_Category==2]~crowdfunding$pAdDeg[crowdfunding$MDS_Category==2]))

```





### Geography Information-simpleplot
```{r Geography Information-simpleplot}
par(mfrow=c(1,2) )

#count_of_Grand.Total
plot(crowdfunding$count_of_Grand.Total,col=crowdfunding$Region, main="Count of Projects Plot",ylab="Count of Projects",xaxt="n",xlab="State")
#axis(side=1,at=c(1,2,3,4,5,6,7,8),labels=c(crowdfunding$State))
legend("topright",legend = levels(crowdfunding$Region),cex = 0.8, pch = 1,col=1:4)



#successful_rate
plot(crowdfunding$successful_rate,col=crowdfunding$Region, main="Successful Rate Plot",ylab="Successful Rate",xaxt="n",xlab="State")
#axis(side=1,at=c(1,2,3,4,5,6,7,8),labels=c(crowdfunding$State))
legend("topleft",legend = levels(crowdfunding$Region),cex = 0.8, pch = 1,col=1:4)
```

### Geography information-boxplot
```{r Geography information-boxplot}
par(mfrow=c(1,3))
#Boxplot for successful_rate and count_of_Grand.Total
#count_of_Grand.Total
boxplot(crowdfunding$count_of_Grand.Total[crowdfunding$Region=="Midwest"],
        crowdfunding$count_of_Grand.Total[crowdfunding$Region=="Northeast"],
        crowdfunding$count_of_Grand.Total[crowdfunding$Region=="South"],
        crowdfunding$count_of_Grand.Total[crowdfunding$Region=="West"],
        names=levels(crowdfunding$Region),main="Count of Projects BoxPlot by Region",varwidth=TRUE)

require(moments)
skewness(crowdfunding_region$count_of_Grand.Total[crowdfunding_region$Region=="Midwest"])
skewness(crowdfunding_region$count_of_Grand.Total[crowdfunding_region$Region=="Northeast"])
skewness(crowdfunding_region$count_of_Grand.Total[crowdfunding_region$Region=="South"])
skewness(crowdfunding_region$count_of_Grand.Total[crowdfunding_region$Region=="West"])

kurtosis(crowdfunding_region$count_of_Grand.Total[crowdfunding_region$Region=="Midwest"])
kurtosis(crowdfunding_region$count_of_Grand.Total[crowdfunding_region$Region=="Northeast"])
kurtosis(crowdfunding_region$count_of_Grand.Total[crowdfunding_region$Region=="South"])
kurtosis(crowdfunding_region$count_of_Grand.Total[crowdfunding_region$Region=="West"])


boxplot(log(crowdfunding_region$count_of_Grand.Total[crowdfunding_region$Region=="Midwest"]),
        log(crowdfunding_region$count_of_Grand.Total[crowdfunding_region$Region=="Northeast"]),
        log(crowdfunding_region$count_of_Grand.Total[crowdfunding_region$Region=="South"]),
        log(crowdfunding_region$count_of_Grand.Total[crowdfunding_region$Region=="West"]),
        names=levels(crowdfunding$Region),main="Log Count of Projects BoxPlot by Region")



#successful_rate
boxplot(crowdfunding$successful_rate[crowdfunding$Region=="Midwest"],
        crowdfunding$successful_rate[crowdfunding$Region=="Northeast"],
        crowdfunding$successful_rate[crowdfunding$Region=="South"],
        crowdfunding$successful_rate[crowdfunding$Region=="West"],
        names=levels(crowdfunding$Region),main="Successful Rate BoxPlot by Region")

skewness(crowdfunding_region$successful_rate[crowdfunding_region$Region=="Midwest"])
skewness(crowdfunding_region$successful_rate[crowdfunding_region$Region=="Northeast"])
skewness(crowdfunding_region$successful_rate[crowdfunding_region$Region=="South"])
skewness(crowdfunding_region$successful_rate[crowdfunding_region$Region=="West"])


kurtosis(crowdfunding_region$successful_rate[crowdfunding_region$Region=="Midwest"])
kurtosis(crowdfunding_region$successful_rate[crowdfunding_region$Region=="Northeast"])
kurtosis(crowdfunding_region$successful_rate[crowdfunding_region$Region=="South"])
kurtosis(crowdfunding_region$successful_rate[crowdfunding_region$Region=="West"])


par(mfrow=c(2,4))
#count_of_Grand.Total
qqnorm(crowdfunding$count_of_Grand.Total[crowdfunding$Region=="Midwest"],xlab="Midwest",xaxt="n",col="black",main="Count of Projects-Norm Q-Q Plot")
qqline(crowdfunding$count_of_Grand.Total[crowdfunding$Region=="Midwest"])

qqnorm(crowdfunding$count_of_Grand.Total[crowdfunding$Region=="Northeast"],xlab="Northeast",xaxt="n",col="red",main="Count of Projectse-Norm Q-Q Plot")
qqline(crowdfunding$count_of_Grand.Total[crowdfunding$Region=="Northeast"])

qqnorm(crowdfunding$count_of_Grand.Total[crowdfunding$Region=="South"],xlab="South",col="green",xaxt="n",main="Count of Projects-Norm Q-Q Plot")
qqline(crowdfunding$count_of_Grand.Total[crowdfunding$Region=="South"])

qqnorm(crowdfunding$count_of_Grand.Total[crowdfunding$Region=="West"],xlab="West",col="blue",xaxt="n",main="Count of Projects-Norm Q-Q Plot")
qqline(crowdfunding$count_of_Grand.Total[crowdfunding$Region=="West"])



#successful_rate
qqnorm(crowdfunding$successful_rate[crowdfunding$Region=="Midwest"],xlab="Midwest",col="black",xaxt="n",main="Successful Rate-Norm Q-Q Plot")
qqline(crowdfunding$successful_rate[crowdfunding$Region=="Midwest"])

qqnorm(crowdfunding$successful_rate[crowdfunding$Region=="Northeast"],xlab="Northeast",col="red",xaxt="n",main="Successful Rate-Norm Q-Q Plot")
qqline(crowdfunding$successful_rate[crowdfunding$Region=="Northeast"])

qqnorm(crowdfunding$successful_rate[crowdfunding$Region=="South"],xlab="South",col="green",xaxt="n",main="Successful Rate-Norm Q-Q Plot")
qqline(crowdfunding$successful_rate[crowdfunding$Region=="South"])

qqnorm(crowdfunding$successful_rate[crowdfunding$Region=="West"],xlab="West",col="blue",xaxt="n",main="Successful Rate-Norm Q-Q Plot")
qqline(crowdfunding$successful_rate[crowdfunding$Region=="West"])

kurtosis(crowdfunding$successful_rate[crowdfunding$Region=="Midwest"])
kurtosis(crowdfunding$successful_rate[crowdfunding$Region=="Northeast"])      
kurtosis(crowdfunding$successful_rate[crowdfunding$Region=="South"])      
kurtosis(crowdfunding$successful_rate[crowdfunding$Region=="West"])      


```
```{r}
par(mfrow=c(1,3))
qqnorm(crowdfunding$successful_rate, main ="Successful rate:Q-Q norm",col="red")
qqline(crowdfunding$successful_rate)
qqnorm(crowdfunding$GiniCoeff, main ="GiniCoeff:Q-Q norm",col="blue")
qqline(crowdfunding$GiniCoeff)
qqnorm(crowdfunding$pAdDeg, main ="pAdDeg:Q-Q norm",col="green")
qqline(crowdfunding$pAdDeg)

cor.test(crowdfunding$successful_rate,crowdfunding$GiniCoeff)
cor.test(crowdfunding$successful_rate,crowdfunding$pAdDeg,method="spearman")

```




### Geography information:factors
```{r}
boxplot(crowdfunding$GiniCoeff[crowdfunding$Region=="Midwest"],crowdfunding$GiniCoeff[crowdfunding$Region=="Northeast"],crowdfunding$GiniCoeff[crowdfunding$Region=="South"],crowdfunding$GiniCoeff[crowdfunding$Region=="West"],names=levels(crowdfunding$Region),main="GiniCoeff BoxPlot by Region",varwidth=TRUE)

boxplot(crowdfunding$pAdDeg[crowdfunding$Region=="Midwest"],crowdfunding$pAdDeg[crowdfunding$Region=="Northeast"],crowdfunding$pAdDeg[crowdfunding$Region=="South"],crowdfunding$pAdDeg[crowdfunding$Region=="West"],names=levels(crowdfunding$Region),main="pAdDeg BoxPlot by Region",varwidth=TRUE)


summary(lm(crowdfunding$successful_rate[crowdfunding$Region=="Midwest"]~crowdfunding$GiniCoeff[crowdfunding$Region=="Midwest"]))
par(mfrow=c(1,4))
#GiniCoeff---------
plot(crowdfunding$successful_rate[crowdfunding$Region=="Midwest"]~crowdfunding$GiniCoeff[crowdfunding$Region=="Midwest"],xlab="GiniCoeff in Midwest",ylab="Successful rate in Midwest")
points(lowess(crowdfunding$successful_rate[crowdfunding$Region=="Midwest"]~crowdfunding$GiniCoeff[crowdfunding$Region=="Midwest"]),col="red",type="l")
plot(crowdfunding$successful_rate[crowdfunding$Region=="West"]~crowdfunding$GiniCoeff[crowdfunding$Region=="West"],xlab="GiniCoeff in West",ylab="Successful rate in West")
points(lowess(crowdfunding$successful_rate[crowdfunding$Region=="West"]~crowdfunding$GiniCoeff[crowdfunding$Region=="West"]),col="red",type="l")

plot(crowdfunding$successful_rate[crowdfunding$Region=="South"]~crowdfunding$GiniCoeff[crowdfunding$Region=="South"],xlab="GiniCoeff in South",ylab="Successful rate in South")
points(lowess(crowdfunding$successful_rate[crowdfunding$Region=="South"]~crowdfunding$GiniCoeff[crowdfunding$Region=="South"]),col="red",type="l")

plot(crowdfunding$successful_rate[crowdfunding$Region=="Northeast"]~crowdfunding$GiniCoeff[crowdfunding$Region=="Northeast"],xlab="GiniCoeff in Northeast",ylab="Successful rate in Northeast")
points(lowess(crowdfunding$successful_rate[crowdfunding$Region=="Northeast"]~crowdfunding$GiniCoeff[crowdfunding$Region=="Northeast"]),col="red",type="l")


#pAdDeg-----------
plot(crowdfunding$successful_rate[crowdfunding$Region=="Midwest"]~crowdfunding$pAdDeg[crowdfunding$Region=="Midwest"],xlab="pAdDeg in Midwest",ylab="Successful rate in Midwest")
points(lowess(crowdfunding$successful_rate[crowdfunding$Region=="Midwest"]~crowdfunding$pAdDeg[crowdfunding$Region=="Midwest"]),col="red",type="l")
plot(crowdfunding$successful_rate[crowdfunding$Region=="West"]~crowdfunding$pAdDeg[crowdfunding$Region=="West"],xlab="pAdDeg in West",ylab="Successful rate in West")
points(lowess(crowdfunding$successful_rate[crowdfunding$Region=="West"]~crowdfunding$pAdDeg[crowdfunding$Region=="West"]),col="red",type="l")

plot(crowdfunding$successful_rate[crowdfunding$Region=="South"]~crowdfunding$pAdDeg[crowdfunding$Region=="South"],xlab="pAdDeg in South",ylab="Successful rate in South")
points(lowess(crowdfunding$successful_rate[crowdfunding$Region=="South"]~crowdfunding$pAdDeg[crowdfunding$Region=="South"]),col="red",type="l")

plot(crowdfunding$successful_rate[crowdfunding$Region=="Northeast"]~crowdfunding$pAdDeg[crowdfunding$Region=="Northeast"],xlab="pAdDeg in Northeast",ylab="Successful rate in Northeast")
points(lowess(crowdfunding$successful_rate[crowdfunding$Region=="Northeast"]~crowdfunding$pAdDeg[crowdfunding$Region=="Northeast"]),col="red",type="l")

#pBatDeg-----------
plot(crowdfunding$successful_rate[crowdfunding$Region=="Midwest"]~crowdfunding$pBatDeg[crowdfunding$Region=="Midwest"],xlab="pBatDeg in Midwest",ylab="Successful rate in Midwest")
points(lowess(crowdfunding$successful_rate[crowdfunding$Region=="Midwest"]~crowdfunding$pBatDeg[crowdfunding$Region=="Midwest"]),col="red",type="l")
plot(crowdfunding$successful_rate[crowdfunding$Region=="West"]~crowdfunding$pBatDeg[crowdfunding$Region=="West"],xlab="pBatDeg in West",ylab="Successful rate in West")
points(lowess(crowdfunding$successful_rate[crowdfunding$Region=="West"]~crowdfunding$pBatDeg[crowdfunding$Region=="West"]),col="red",type="l")

plot(crowdfunding$successful_rate[crowdfunding$Region=="South"]~crowdfunding$pBatDeg[crowdfunding$Region=="South"],xlab="pBatDeg in South",ylab="Successful rate in South")
points(lowess(crowdfunding$successful_rate[crowdfunding$Region=="South"]~crowdfunding$pBatDeg[crowdfunding$Region=="South"]),col="red",type="l")

plot(crowdfunding$successful_rate[crowdfunding$Region=="Northeast"]~crowdfunding$pBatDeg[crowdfunding$Region=="Northeast"],xlab="pBatDeg in Northeast",ylab="Successful rate in Northeast")
points(lowess(crowdfunding$successful_rate[crowdfunding$Region=="Northeast"]~crowdfunding$pBatDeg[crowdfunding$Region=="Northeast"]),col="red",type="l")

#pHigh-----------
plot(crowdfunding$successful_rate[crowdfunding$Region=="Midwest"]~crowdfunding$pHigh[crowdfunding$Region=="Midwest"],xlab="pHigh in Midwest",ylab="Successful rate in Midwest")
points(lowess(crowdfunding$successful_rate[crowdfunding$Region=="Midwest"]~crowdfunding$pHigh[crowdfunding$Region=="Midwest"]),col="red",type="l")
plot(crowdfunding$successful_rate[crowdfunding$Region=="West"]~crowdfunding$pHigh[crowdfunding$Region=="West"],xlab="pHigh in West",ylab="Successful rate in West")
points(lowess(crowdfunding$successful_rate[crowdfunding$Region=="West"]~crowdfunding$pHigh[crowdfunding$Region=="West"]),col="red",type="l")

plot(crowdfunding$successful_rate[crowdfunding$Region=="South"]~crowdfunding$pHigh[crowdfunding$Region=="South"],xlab="pHigh in South",ylab="Successful rate in South")
points(lowess(crowdfunding$successful_rate[crowdfunding$Region=="South"]~crowdfunding$pHigh[crowdfunding$Region=="South"]),col="red",type="l")

plot(crowdfunding$successful_rate[crowdfunding$Region=="Northeast"]~crowdfunding$pHigh[crowdfunding$Region=="Northeast"],xlab="pHigh in Northeast",ylab="Successful rate in Northeast")
points(lowess(crowdfunding$successful_rate[crowdfunding$Region=="Northeast"]~crowdfunding$pHigh[crowdfunding$Region=="Northeast"]),col="red",type="l")

```

```{r}
par(mfrow=c(1,4))
#GiniCoeff---------
plot(crowdfunding$successful_rate[crowdfunding$Region=="Midwest"]~crowdfunding$GiniCoeff[crowdfunding$Region=="Midwest"],xlab="GiniCoeff in Midwest",ylab="Successful rate in Midwest")
points(lowess(crowdfunding$successful_rate[crowdfunding$Region=="Midwest"]~crowdfunding$GiniCoeff[crowdfunding$Region=="Midwest"]),col="red",type="l")
plot(crowdfunding$successful_rate[crowdfunding$Region=="West"]~crowdfunding$GiniCoeff[crowdfunding$Region=="West"],xlab="GiniCoeff in West",ylab="Successful rate in West")
points(lowess(crowdfunding$successful_rate[crowdfunding$Region=="West"]~crowdfunding$GiniCoeff[crowdfunding$Region=="West"]),col="red",type="l")

plot(crowdfunding$successful_rate[crowdfunding$Region=="South"]~crowdfunding$GiniCoeff[crowdfunding$Region=="South"],xlab="GiniCoeff in South",ylab="Successful rate in South")
points(lowess(crowdfunding$successful_rate[crowdfunding$Region=="South"]~crowdfunding$GiniCoeff[crowdfunding$Region=="South"]),col="red",type="l")

plot(crowdfunding$successful_rate[crowdfunding$Region=="Northeast"]~crowdfunding$GiniCoeff[crowdfunding$Region=="Northeast"],xlab="GiniCoeff in Northeast",ylab="Successful rate in Northeast")
points(lowess(crowdfunding$successful_rate[crowdfunding$Region=="Northeast"]~crowdfunding$GiniCoeff[crowdfunding$Region=="Northeast"]),col="red",type="l")


#pAdDeg-----------
plot(crowdfunding$successful_rate[crowdfunding$Region=="Midwest"]~crowdfunding$pAdDeg[crowdfunding$Region=="Midwest"],xlab="pAdDeg in Midwest",ylab="Successful rate in Midwest")
points(lowess(crowdfunding$successful_rate[crowdfunding$Region=="Midwest"]~crowdfunding$pAdDeg[crowdfunding$Region=="Midwest"]),col="red",type="l")
plot(crowdfunding$successful_rate[crowdfunding$Region=="West"]~crowdfunding$pAdDeg[crowdfunding$Region=="West"],xlab="pAdDeg in West",ylab="Successful rate in West")
points(lowess(crowdfunding$successful_rate[crowdfunding$Region=="West"]~crowdfunding$pAdDeg[crowdfunding$Region=="West"]),col="red",type="l")

plot(crowdfunding$successful_rate[crowdfunding$Region=="South"]~crowdfunding$pAdDeg[crowdfunding$Region=="South"],xlab="pAdDeg in South",ylab="Successful rate in South")
points(lowess(crowdfunding$successful_rate[crowdfunding$Region=="South"]~crowdfunding$pAdDeg[crowdfunding$Region=="South"]),col="red",type="l")

plot(crowdfunding$successful_rate[crowdfunding$Region=="Northeast"]~crowdfunding$pAdDeg[crowdfunding$Region=="Northeast"],xlab="pAdDeg in Northeast",ylab="Successful rate in Northeast")
points(lowess(crowdfunding$successful_rate[crowdfunding$Region=="Northeast"]~crowdfunding$pAdDeg[crowdfunding$Region=="Northeast"]),col="red",type="l")

#pBatDeg-----------
plot(crowdfunding$successful_rate[crowdfunding$Region=="Midwest"]~crowdfunding$pBatDeg[crowdfunding$Region=="Midwest"],xlab="pBatDeg in Midwest",ylab="Successful rate in Midwest")
points(lowess(crowdfunding$successful_rate[crowdfunding$Region=="Midwest"]~crowdfunding$pBatDeg[crowdfunding$Region=="Midwest"]),col="red",type="l")
plot(crowdfunding$successful_rate[crowdfunding$Region=="West"]~crowdfunding$pBatDeg[crowdfunding$Region=="West"],xlab="pBatDeg in West",ylab="Successful rate in West")
points(lowess(crowdfunding$successful_rate[crowdfunding$Region=="West"]~crowdfunding$pBatDeg[crowdfunding$Region=="West"]),col="red",type="l")

plot(crowdfunding$successful_rate[crowdfunding$Region=="South"]~crowdfunding$pBatDeg[crowdfunding$Region=="South"],xlab="pBatDeg in South",ylab="Successful rate in South")
points(lowess(crowdfunding$successful_rate[crowdfunding$Region=="South"]~crowdfunding$pBatDeg[crowdfunding$Region=="South"]),col="red",type="l")

plot(crowdfunding$successful_rate[crowdfunding$Region=="Northeast"]~crowdfunding$pBatDeg[crowdfunding$Region=="Northeast"],xlab="pBatDeg in Northeast",ylab="Successful rate in Northeast")
points(lowess(crowdfunding$successful_rate[crowdfunding$Region=="Northeast"]~crowdfunding$pBatDeg[crowdfunding$Region=="Northeast"]),col="red",type="l")

#pHigh-----------
plot(crowdfunding$successful_rate[crowdfunding$Region=="Midwest"]~crowdfunding$pHigh[crowdfunding$Region=="Midwest"],xlab="pHigh in Midwest",ylab="Successful rate in Midwest")
points(lowess(crowdfunding$successful_rate[crowdfunding$Region=="Midwest"]~crowdfunding$pHigh[crowdfunding$Region=="Midwest"]),col="red",type="l")
plot(crowdfunding$successful_rate[crowdfunding$Region=="West"]~crowdfunding$pHigh[crowdfunding$Region=="West"],xlab="pHigh in West",ylab="Successful rate in West")
points(lowess(crowdfunding$successful_rate[crowdfunding$Region=="West"]~crowdfunding$pHigh[crowdfunding$Region=="West"]),col="red",type="l")

plot(crowdfunding$successful_rate[crowdfunding$Region=="South"]~crowdfunding$pHigh[crowdfunding$Region=="South"],xlab="pHigh in South",ylab="Successful rate in South")
points(lowess(crowdfunding$successful_rate[crowdfunding$Region=="South"]~crowdfunding$pHigh[crowdfunding$Region=="South"]),col="red",type="l")

plot(crowdfunding$successful_rate[crowdfunding$Region=="Northeast"]~crowdfunding$pHigh[crowdfunding$Region=="Northeast"],xlab="pHigh in Northeast",ylab="Successful rate in Northeast")
points(lowess(crowdfunding$successful_rate[crowdfunding$Region=="Northeast"]~crowdfunding$pHigh[crowdfunding$Region=="Northeast"]),col="red",type="l")


```

### Geography information-t.test
```{r Geography information-t.test}
#t.test(crowdfunding$successful_rate[crowdfunding$Region=="West"],crowdfunding$successful_rate[crowdfunding$Region=="Northeast"])
#t.test(crowdfunding$count_of_Grand.Total[crowdfunding$Region=="West"],crowdfunding$count_of_Grand.Total[crowdfunding$Region=="Northeast"])


#calcualte P Value in the t.test of Successful Rate by Region 

p=NULL
temp<-NULL
for (location1 in c(levels(crowdfunding$Region))){
  for (location2 in c(levels(crowdfunding$Region))){
    if (1){
      temp<-t.test(crowdfunding$successful_rate[crowdfunding$Region==location1],crowdfunding$successful_rate[crowdfunding$Region==location2])
      if(temp$p.value<=0.1){
        #print(c(location1,location2,temp$p.value))
      }
      p<-c(p,temp$p.value)}}}
SR.t.test.p.vlaue<-as.data.frame(matrix(p,4,4),row.names = c(levels(crowdfunding$Region)))
colnames(SR.t.test.p.vlaue)<-c(levels(crowdfunding$Region))
print("Successful Rate by Region")
SR.t.test.p.vlaue
#--------------------------------------------------------
#calcualte P Value in the t.test of Count of projects by Region 

p=NULL
temp<-NULL
for (location1 in c(levels(crowdfunding$Region))){
  for (location2 in c(levels(crowdfunding$Region))){
    if (1){
      temp<-t.test(log(crowdfunding$count_of_Grand.Total[crowdfunding$Region==location1]),log(crowdfunding$count_of_Grand.Total[crowdfunding$Region==location2]))
      if(temp$p.value<=0.1){
        #print(c(location1,location2,temp$p.value))
      }
      p<-c(p,temp$p.value)}}}
CP.t.test.p.vlaue<-as.data.frame(matrix(p,4,4),row.names = c(levels(crowdfunding$Region)))
colnames(CP.t.test.p.vlaue)<-c(levels(crowdfunding$Region))
print("Count of Projects by Region ")
CP.t.test.p.vlaue
#--------------------------------------------------------
#calcualte P Value in the t.test of Count of projects by kmeans 
"MDS-Category"

p=NULL

temp<-NULL
#for (location1 in 1:4){
#  for (location2 in 1:4){
#    if (1){
#      temp<-t.test(crowdfunding$successful_rate[crowdfunding$MDS_Category==location1],crowdfunding$successful_rate[crowdfunding$MDS_Category==location2])
#      if(temp$p.value<=0.1){
#        print(c(location1,location2,temp$p.value))
#      }
#      p<-c(p,temp$p.value)}}}
#MDSR.t.test.p.vlaue<-as.data.frame(matrix(p,4,4),row.names = c(1:4))
#colnames(MDSR.t.test.p.vlaue)<-c(1:4)
#print("Successful Rate by Region")
#MDSR.t.test.p.vlaue


```

### Geography information-t-bootstrap
```{r}
bootstrapfunctiontest<-function(target.sample,n){
  temp=0
  for(i in 1:n){
    temp[i]=mean(target.sample[sample(1:length(target.sample),replace = TRUE)])
  }
  return (temp)
}

#Successful rate
Northeast.SR.bootstrap=bootstrapfunctiontest(crowdfunding$successful_rate[crowdfunding$Region=="Northeast"],1000)
West.SR.bootstrap=bootstrapfunctiontest(crowdfunding$successful_rate[crowdfunding$Region=="West"],1000)
Midwest.SR.bootstrap=bootstrapfunctiontest(crowdfunding$successful_rate[crowdfunding$Region=="Midwest"],1000)
South.SR.bootstrap=bootstrapfunctiontest(crowdfunding$successful_rate[crowdfunding$Region=="South"],1000)
Northeast.West.SR.t<-t.test(Northeast.SR.bootstrap,West.SR.bootstrap)
Northeast.South.SR.t<-t.test(Northeast.SR.bootstrap,South.SR.bootstrap)
Northeast.Midwest.SR.t<-t.test(Northeast.SR.bootstrap,Midwest.SR.bootstrap)
West.Midwest.SR.t<-t.test(West.SR.bootstrap,Midwest.SR.bootstrap)
West.South.SR.t<-t.test(West.SR.bootstrap,South.SR.bootstrap)
Midwest.South.SR.t<-t.test(Midwest.SR.bootstrap,South.SR.bootstrap)
#sink("/Users/sn0wfree/Dropbox/PhD_1st_study/BST215_Quantitative_Research_Methods_Term_1/QRM_assignment/result/t.bootstrap.txt")
t.test(Northeast.SR.bootstrap,West.SR.bootstrap)
t.test(Northeast.SR.bootstrap,South.SR.bootstrap)
t.test(Northeast.SR.bootstrap,Midwest.SR.bootstrap)
t.test(West.SR.bootstrap,Midwest.SR.bootstrap)
t.test(West.SR.bootstrap,South.SR.bootstrap)
t.test(Midwest.SR.bootstrap,South.SR.bootstrap)
#sink()
hist(West.SR.bootstrap)









#count of project
Northeast.CP.bootstrap=bootstrapfunctiontest(crowdfunding$count_of_Grand.Total[crowdfunding$Region=="Northeast"],1000)
West.CP.bootstrap=bootstrapfunctiontest(crowdfunding$count_of_Grand.Total[crowdfunding$Region=="West"],1000)
Midwest.CP.bootstrap=bootstrapfunctiontest(crowdfunding$count_of_Grand.Total[crowdfunding$Region=="Midwest"],1000)
South.CP.bootstrap=bootstrapfunctiontest(crowdfunding$count_of_Grand.Total[crowdfunding$Region=="South"],1000)
t.test(Northeast.CP.bootstrap,West.CP.bootstrap)
t.test(Northeast.CP.bootstrap,South.CP.bootstrap)
t.test(Northeast.CP.bootstrap,Midwest.CP.bootstrap)
t.test(West.CP.bootstrap,Midwest.CP.bootstrap)
t.test(West.CP.bootstrap,South.CP.bootstrap)
t.test(Midwest.CP.bootstrap,South.CP.bootstrap)
```




### Geography information-wilcox.test
```{r Geography information-wilcox.test}
wilcox.test(crowdfunding$successful_rate[crowdfunding$Region=="West"],crowdfunding$successful_rate[crowdfunding$Region=="Northeast"])
wilcox.test(crowdfunding$successful_rate[crowdfunding$Region=="Midwest"],crowdfunding$successful_rate[crowdfunding$Region=="Northeast"])
wilcox.test(crowdfunding$successful_rate[crowdfunding$Region=="South"],crowdfunding$successful_rate[crowdfunding$Region=="Northeast"])
wilcox.test(crowdfunding$successful_rate[crowdfunding$Region=="West"],crowdfunding$successful_rate[crowdfunding$Region=="Midwest"])


#wilcox.test(crowdfunding$count_of_Grand.Total[crowdfunding$Region=="West"],crowdfunding$count_of_Grand.Total[crowdfunding$Region=="Northeast"])


#calcualte P Value in the wilcox.test of Successful Rate by Region 

p=NULL
temp<-NULL
for (location1 in c(levels(crowdfunding$Region))){
  for (location2 in c(levels(crowdfunding$Region))){
    if (1){
      temp<-wilcox.test(crowdfunding$successful_rate[crowdfunding$Region==location1],crowdfunding$successful_rate[crowdfunding$Region==location2])
      if(temp$p.value<=0.1){
        #print(c(location1,location2,temp$p.value))
      }
      p<-c(p,temp$p.value)}}}
SR.wilcox.test.p.vlaue<-as.data.frame(matrix(p,4,4),row.names = c(levels(crowdfunding$Region)))
colnames(SR.wilcox.test.p.vlaue)<-c(levels(crowdfunding$Region))
print("Successful Rate by Region")
SR.wilcox.test.p.vlaue
#--------------------------------------------------------
#calcualte P Value in the t.test of Count of projects by Region 

p=NULL
temp<-NULL
for (location1 in c(levels(crowdfunding$Region))){
  for (location2 in c(levels(crowdfunding$Region))){
    if (1){
      temp<-wilcox.test(log(crowdfunding$count_of_Grand.Total[crowdfunding$Region==location1]),log(crowdfunding$count_of_Grand.Total[crowdfunding$Region==location2]))
      if(temp$p.value<=0.1){
        #print(c(location1,location2,temp$p.value))
      }
      p<-c(p,temp$p.value)}}}
CP.wilcox.test.p.vlaue<-as.data.frame(matrix(p,4,4),row.names = c(levels(crowdfunding$Region)))
colnames(CP.wilcox.test.p.vlaue)<-c(levels(crowdfunding$Region))
print("Count of Projects by Region ")
CP.wilcox.test.p.vlaue
#--------------------------------------------------------
#calcualte P Value in the t.test of Count of projects by kmeans 
"MDS-Category"
p=NULL

temp<-NULL
#for (location1 in 1:4){
#  for (location2 in 1:4){
#    if (1){
#      temp<-wilcox.test(crowdfunding$successful_rate[crowdfunding$MDS_Category==location1],crowdfunding$successful_rate[crowdfunding$MDS_Category==location2])
#      if(temp$p.value<=0.1){
#        print(c(location1,location2,temp$p.value))
#      }
#      p<-c(p,temp$p.value)}}}
#MDSR.wilcox.test.p.vlaue<-as.data.frame(matrix(p,4,4),row.names = c(1:4))
#colnames(MDSR.wilcox.test.p.vlaue)<-c(1:4)
#print("Successful Rate by Region")
#MDSR.wilcox.test.p.vlaue
```



## Factors analysis
This article is to analyse the factors to the crowdfunding successful rate.
I guess the Education, the inequity of family income and the poverty rate may be related to the crowdfunding successful rate. and in the follow context, i will analyse the those factors.

Firstly, The Statistical Summary
### Factors Analysis-Statistical Summary
```{r Factors Analysis-Statistical Summary}
library(moments)

summary(crowdfunding$successful_rate)
kurtosis(crowdfunding$successful_rate)

summary(crowdfunding$GiniCoeff)
kurtosis(crowdfunding$GiniCoeff)

summary(crowdfunding$pAdDeg)
kurtosis(crowdfunding$pAdDeg)

summary(crowdfunding$PovRate1)
kurtosis(crowdfunding$PovRate1)

```

### Factors Analysis-Plot for Factors
```{r Plot for Factors}

boxplot(crowdfunding$successful_rate,crowdfunding$GiniCoeff,crowdfunding$pAdDeg,crowdfunding$PovRate1,names = c("Successful Rate","GiniCoeff","Higher Education","PovRate1"),main="Factors Box Plot")

par(mfrow=c(1,4))
plot(crowdfunding$successful_rate,col=crowdfunding$Region,pch=10,cex=1.5,xlab="State",ylab="Successful Rate",xaxt="n",main="Successful Rate Plot with lowess line")
points(lowess(crowdfunding$successful),type="l")
legend("topleft",legend = levels(crowdfunding$Region),cex = 0.8, pch = 1,col=1:4)

plot(crowdfunding$GiniCoeff,col=crowdfunding$Region,pch=18,cex=1.5,xlab="State",xaxt="n",ylab="GiniCoeff ",xaxt="n",main="GiniCoeff Plot with lowess line")
points(lowess(crowdfunding$GiniCoeff),type="l")
legend("topleft",legend = levels(crowdfunding$Region),cex = 0.8, pch = 1,col=1:4)

plot(crowdfunding$pAdDeg,col=crowdfunding$Region,pch=15,cex=1.5,xlab="State",xaxt="n",ylab="Adanced Education Rate",xaxt="n",main="Adanced Education Rate Plot with lowess line")
points(lowess(crowdfunding$pAdDeg),type="l")
legend("topright",legend = levels(crowdfunding$Region),cex = 0.8, pch = 1,col=1:4)

plot(crowdfunding$pAdDeg,col=crowdfunding$Region,pch=15,cex=1.5,xlab="State",xaxt="n",ylab="Adanced Education Rate",xaxt="n",main="Adanced Education Rate Plot with lowess line")
points(lowess(crowdfunding$pAdDeg),type="l")
legend("topright",legend = levels(crowdfunding$Region),cex = 0.8, pch = 1,col=1:4)

require(car)
scatterplotMatrix(~successful_rate+PovRate1+pAdDeg,data=crowdfunding,spread=1,upper.panel=NULL,main="Scatter Plot Matrix on Factors")

require(moments)
kurtosis(crowdfunding$pAdDeg)
#plot(crowdfunding$PovRate1,col="black",pch=16,xlab="State",xaxt="n",ylab="Poverty Rate",xaxt="n",main="Poverty Rate Plot")
#library(gridExtra)

summary(lm(log(successful_rate)~log(Collectivism),data=crowdfunding))
ggplot(crowdfunding,aes(y=successful_rate,x=Collectivism))+geom_point()+geom_smooth(method="lm")

```





```{r scatterplot}
library(car)
scatterplot(crowdfunding$successful_rate,log(crowdfunding$average_of_goal_Grand.Total),pch=19)
scatterplot(crowdfunding$successful_rate~crowdfunding$GiniCoeff,pch=19)
scatterplot(crowdfunding$successful_rate~crowdfunding$PovRate1,pch=19)
scatterplot(crowdfunding$successful_rate~crowdfunding$Densitym2,pch=19)
scatterplot(crowdfunding$successful_rate~crowdfunding$pHigh,pch=19)
scatterplot(crowdfunding$successful_rate~crowdfunding$pBatDeg,pch=19)
scatterplot(crowdfunding$successful_rate~crowdfunding$pAdDeg,pch=19)


```


### Factors Analysis-Successful Rate|PovRate1
```{r Successful Rate-PovRate1}
#redo scatterplot with Successful Rate-PovRate1
scatterplot(crowdfunding$successful_rate,crowdfunding$PovRate1,pch=19)

#anova(successful_rate2PovRate1)
library(ggplot2)
ggplot(crowdfunding,aes(x=PovRate1,y=successful_rate,main = "Successful rate~PovRate"))+geom_point(aes(col=Region))+geom_smooth(method = "loess")+ggtitle("Successful Rate-PovRate1 Plot")+theme(plot.title=element_text(hjust=0.5))

par(mfrow=c(1,2))

boxplot(crowdfunding$successful_rate,crowdfunding$PovRate1,names=c("Successful Rate","PovRate1"))
boxplot(crowdfunding$successful_rate[crowdfunding$PovRate1>mean(crowdfunding$PovRate1)],crowdfunding$successful_rate[crowdfunding$PovRate1<=mean(crowdfunding$PovRate1)],col = c("green","deepskyblue"),names=c("Successful%(High PovRate)","Successful%(Low PovRate)"),xlab="Successful rate by PovRate1")



t.test(crowdfunding$successful_rate[crowdfunding$PovRate1>mean(crowdfunding$PovRate1)],crowdfunding$successful_rate[crowdfunding$PovRate1<=mean(crowdfunding$PovRate1)])



plot(crowdfunding$PovRate1,crowdfunding$successful_rate,pch=19,col=crowdfunding$Region,ylab="Successful Rate",xlab="PovRate1",main="Successful Rate-PovRate1 Plot with lowess line")
points(lowess(crowdfunding$PovRate1,crowdfunding$successful_rate,f=1/3),pch=4,col="orange",type="l")
#abline(lm(crowdfunding$successful_rate~crowdfunding$PovRate1),col="orange")
legend("bottomright",legend = levels(crowdfunding$Region),cex = 0.8, pch = 19,col=1:4)


qqplot(crowdfunding$PovRate1,crowdfunding$successful_rate,pch=1,col=crowdfunding$Region,main="QQ plot: (Successful Rate & PovRate)")
qqline(crowdfunding$PovRate1 ,crowdfunding$successful_rate,col="red")
legend("topleft",legend = levels(crowdfunding$Region), pch = 19,col=1:3)




par(mfrow=c(1,1))
#qqnorm(crowdfunding$successful_rate,col=crowdfunding$Region,xlab="Successful Rate")
#qqline(crowdfunding$successful_rate,col="red")
qqnorm(crowdfunding$PovRate1,col=crowdfunding$Region,pch=18,xlab ="PovRate1")
qqline(crowdfunding$PovRate1,col="red")
```

```{r successful_rate-PovRate1:ggplot}
require(ggplot2)
ggplot(crowdfunding,aes(x=PovRate1,y=successful_rate,main = "Successful rate~PovRate1"))+geom_point(aes(group=1,col=Region))+geom_smooth(method = "loess",se=FALSE,aes(group=2,col="Loess Line"),lty="dashed")+geom_smooth(method = "lm",aes(group=2,col="Regression Line"),cex=0.8,lty=1)+ggtitle("Successful Rate-PovRate1 Plot with Loess line and OLS-estimated line")+labs(x="PovRate1",y="Successful Rate")+theme(plot.title=element_text(hjust=0.5))+scale_colour_manual(values = c("purple", "magenta", "orange","red", "blue", "green3" ),guide = guide_legend(title="Legend",override.aes = list(linetype = c( "dashed",rep("blank", 2), "solid",rep("blank", 2)),shape = c(NA,rep(16, 2), NA,rep(16, 2) ))))



```



```{r reset-Chow test}
require(lmtest)
require(strucchange)
resettest(crowdfunding$successful_rate~crowdfunding$GiniCoeff+(crowdfunding$GiniCoeff)^2)

sctest(log(crowdfunding$successful_rate)~log(crowdfunding$GiniCoeff),type="Chow")

resettest(log(crowdfunding$successful_rate)~log(crowdfunding$pAdDeg))

sctest(crowdfunding$successful_rate~crowdfunding$pAdDeg,type="Chow",point=0.12)
plot(crowdfunding$successful_rate~crowdfunding$pAdDeg)
points(lowess(crowdfunding$successful_rate~crowdfunding$pAdDeg),type="l")
```

```{r testfornothing}
b=NA
for (i in 150:170){
a<-sctest(log(crowdfunding$successful_rate)~log(crowdfunding$pAdDeg),type="Chow",point=i/1000)
b[i]=a$p.value
#if (a$p.value<=0.05){print (i/100)}
}
plot(b,type="l")
b[9]
sctest(log(crowdfunding$successful_rate)~log(crowdfunding$pAdDeg),type="Chow",point=0.17)
```

### Factors Analysis-Successful Rate|GiniCoeff
```{r Successful Rate-GiniCoeff}

ggplot(crowdfunding,aes(x=GiniCoeff,y=successful_rate,main = "Successful rate~GiniCoeff"))+geom_point(group=1,aes(col=Region))+geom_smooth(method = "loess",se=FALSE,lty="dashed",aes(group=2,col="Trendline"))+geom_smooth(method = "lm",aes(group=2,col="Regression"),lty=1,cex=0.5)+ggtitle("Successful Rate-GiniCoeff Plot with loess line and OLS-estimated line")+labs(x="GiniCoeff",y="Successful Rate")+theme(plot.title=element_text(hjust=0.5))


anova(successful_rate2GiniCoeff)


t.test(crowdfunding$successful_rate[crowdfunding$GiniCoeff>mean(crowdfunding$GiniCoeff)],crowdfunding$successful_rate[crowdfunding$GiniCoeff<=mean(crowdfunding$GiniCoeff)])

par(mfrow=c(1,2))


boxplot(crowdfunding$successful_rate,crowdfunding$GiniCoeff,names=c("Successful Rate","GiniCoeff"))
boxplot(crowdfunding$successful_rate[crowdfunding$GiniCoeff>mean(crowdfunding$GiniCoeff)],crowdfunding$successful_rate[crowdfunding$GiniCoeff<=mean(crowdfunding$GiniCoeff)],col = c("darkorchid2","dodgerblue"),names=c("Successful%(High GiniCoeff)","Successful%(Low GiniCoeff)"),xlab="Successful rate by GiniCoeff")







plot(crowdfunding$successful_rate,crowdfunding$GiniCoeff,f=1/3 ,pch=19,col="blue",xlab="Successful Rate",ylab="GiniCoeff",main="Successful Rate-GiniCoeff Plot with lowess line")
points(lowess(crowdfunding$successful_rate,crowdfunding$GiniCoeff,f=1/3),pch=4,col="red",type="l")


qqplot(crowdfunding$GiniCoeff,crowdfunding$successful_rate,pch=18,main="Q-Q Plot: Successful Rate-GiniCoeff with lowess line",xlab="Gini Coefficient",ylab="Successful Rate")
points(lowess(crowdfunding$GiniCoeff,crowdfunding$successful_rate),type="l",col="red")

#qqline(crowdfunding$successful_rate,crowdfunding$GiniCoeff)


#qqnorm(crowdfunding$successful_rate,col="orange",xlab="Successful Rate")
#qqline(crowdfunding$successful_rate,col="red")
par(mfrow=c(1,1))
qqnorm(crowdfunding$GiniCoeff,col="blue",pch=20,xlab="GiniCoeff")
qqline(crowdfunding$GiniCoeff,col="red")


summary(lm(crowdfunding$successful_rate~crowdfunding$GiniCoeff))

```


```{r successful_rate-GiniCoeff:ggplot}

ggplot(crowdfunding,aes(x=GiniCoeff,y=successful_rate,main = "Successful rate~GiniCoeff"))+geom_point(aes(group=1,col=Region))+geom_smooth(method = "loess",se=FALSE,aes(group=2,col="Loess Line"),lty="dashed")+geom_smooth(method = "lm",aes(group=2,col="Regression Line"),cex=0.8,lty=1)+ggtitle("Successful Rate-GiniCoeff Plot with Loess line and OLS-estimated line")+labs(x="GiniCoeff",y="Successful Rate")+theme(plot.title=element_text(hjust=0.5))+scale_colour_manual(values = c("purple", "magenta", "orange","red", "blue", "green3" ),guide = guide_legend(title="Legend",override.aes = list(linetype = c( "dashed",rep("blank", 2), "solid",rep("blank", 2)),shape = c(NA,rep(16, 2), NA,rep(16, 2) ))))



```




### Factors Analysis-Successful Rate|Advanced Education]
 
```{r successful_rate-pAdDeg:ggplot}
ggplot(crowdfunding,aes(x=pAdDeg,y=successful_rate))+geom_point(aes(col=Region))+geom_smooth(method = "loess",se=FALSE,lty=2,aes(col="Loess Line"))+geom_smooth(method = "lm",aes(col="Regression Line"),cex=0.8)+ggtitle("Successful Rate-Adanced Education Plot with Loess line and OLS-estimated line")+labs(x="Advanced Education",y="Successful Rate")+theme(plot.title=element_text(hjust=0.5))+scale_colour_manual(values = c("purple", "magenta","orange", "red", "blue", "green3" ),guide = guide_legend(title="Legend",override.aes = list(linetype = c( "dashed",rep("blank", 2), "solid",rep("blank", 2)),shape = c( NA,rep(16, 2), NA,rep(16, 2)))))
```


```{r Successful Rate-Advanced Education}
ggplot(crowdfunding,aes(x=pAdDeg,y=successful_rate))+geom_point(aes(col=Region))+geom_smooth(method = "loess",se=FALSE,lty=2)+geom_smooth(method = "lm",col="red",cex=0.8)+ggtitle("Successful Rate-Adanced Education Plot")+labs(x="Advanced Education",y="Successful Rate")+theme(plot.title=element_text(hjust=0.5))

plot(lowess(crowdfunding$pAdDeg,crowdfunding$successful_rate))

anova(successful_rate2pAdDeg)

par(mfrow=c(1,2))

boxplot(crowdfunding$successful_rate,crowdfunding$pAdDeg,names=c("Successful rate","Adanced Education"))
boxplot(crowdfunding$successful_rate[crowdfunding$pAdDeg>mean(crowdfunding$pAdDeg)],crowdfunding$successful_rate[crowdfunding$pAdDeg<=mean(crowdfunding$pAdDeg)],col = c("darkorchid2","dodgerblue"),names=c("Successful%(High Adanced Education)","Successful%(Low Adanced Education)"),xlab="Successful rate by Adanced Education")


t.test(crowdfunding$successful_rate[crowdfunding$pAdDeg>mean(crowdfunding$pAdDeg)],crowdfunding$successful_rate[crowdfunding$pAdDeg<=mean(crowdfunding$pAdDeg)])


plot(crowdfunding$successful_rate,crowdfunding$pAdDeg,f=1/3 ,pch=19,col="blue",xlab="Successful Rate",ylab="Adanced Education",main="Successful Rate-Adanced Education Plot with lowess line")
points(lowess(crowdfunding$successful_rate,crowdfunding$pAdDeg,f=1/3),pch=4,col="red",type="l")


qqplot(crowdfunding$successful_rate,crowdfunding$pAdDeg,pch=19,col="red",main="Q-Q Plot: Successful Rate-Adanced Education")
qqline(crowdfunding$successful_rate,crowdfunding$pAdDeg)


#qqnorm(crowdfunding$successful_rate,col="orange",xlab="Successful Rate")
#qqline(crowdfunding$successful_rate,col="red")
par(mfrow=c(1,1))
qqnorm(crowdfunding$pAdDeg,col="blue",pch=20,xlab="Adanced Education")
qqline(crowdfunding$pAdDeg,col="red")

summary(lm(crowdfunding$successful_rate~crowdfunding$pAdDeg))

```
### Factors Analysis- Ginicoeff|Advanced Education

```{r Ginicoeff|Advanced Education}
require(ggplot2)
ggplot(crowdfunding,aes(x=pAdDeg,y=GiniCoeff))+geom_point(aes(col=Region))+geom_smooth(method = "loess",se=FALSE,lty=2)+geom_smooth(method = "lm",col="red",cex=0.8)+ggtitle("Successful Rate-Adanced Education Plot")+labs(x="Advanced Education",y="GiniCoeff")+theme(plot.title=element_text(hjust=0.5))

plot(lowess(crowdfunding$pAdDeg,crowdfunding$GiniCoeff))



par(mfrow=c(1,2))

boxplot(crowdfunding$GiniCoeff,crowdfunding$pAdDeg,names=c("Successful rate","Adanced Education"))
boxplot(crowdfunding$GiniCoeff[crowdfunding$pAdDeg>mean(crowdfunding$pAdDeg)],crowdfunding$GiniCoeff[crowdfunding$pAdDeg<=mean(crowdfunding$pAdDeg)],col = c("darkorchid2","dodgerblue"),names=c("GiniCoeff(High Adanced Education)","GiniCoeff(Low Adanced Education)"),xlab="GiniCoeff by Adanced Education")


t.test(crowdfunding$GiniCoeff[crowdfunding$pAdDeg>mean(crowdfunding$pAdDeg)],crowdfunding$GiniCoeff[crowdfunding$pAdDeg<=mean(crowdfunding$pAdDeg)])


plot(crowdfunding$GiniCoeff,crowdfunding$pAdDeg,f=1/3 ,pch=19,col="blue",xlab="Successful Rate",ylab="Adanced Education",main="GiniCoeff-Adanced Education Plot with lowess line")
points(lowess(crowdfunding$GiniCoeff,crowdfunding$pAdDeg,f=1/3),pch=4,col="red",type="l")


qqplot(crowdfunding$GiniCoeff,crowdfunding$pAdDeg,pch=19,col="red",main="Q-Q Plot: GiniCoeff-Adanced Education")
qqline(crowdfunding$GiniCoeff,crowdfunding$pAdDeg)


#qqnorm(crowdfunding$GiniCoeff,col="orange",xlab="GiniCoeff")
#qqline(crowdfunding$GiniCoeff,col="red")
par(mfrow=c(1,1))
qqnorm(crowdfunding$pAdDeg,col="blue",pch=20,xlab="Adanced Education")
qqline(crowdfunding$pAdDeg,col="red")



```


```{r}

require(corpcor)
require(ggm)


xcor=cor(se)#相关系数矩阵 
xpcor=cor2pcor(xcor)#偏相关矩阵
xpcor
xcor
require(psych)
u<-colnames(xcor)
u


pcortest_dev<-function(x_set,y,dataset){
  require(psych)
  require(ggm)
  for (nv in length(x_set) ){
    
    data_subset<-dataset
    data_subset[x_set[nv]]<-NULL
    x_subset=x_set
    x_subset=x_subset[-nv]
    jsbl <- c(x_subset,y)   #要计算的相关系数的变量下标
    tjbl <- c(x_set[nv])  #条件(控制)变量的下标，即要排除影响的变量的下标
    r<-pcor(c(jsbl,tjbl),cor(dataset))
    q <- length(tjbl) 
    n <- dim(dataset)[1]
    print(x_set[nv])
    pcor_test<- pcor.test(r,q,n)
   
    print(pcor_test)
  }
  return(pcor_test)
}
pcortest_dev(c("GiniCoeff","pAdDeg"),c("successful_rate"),se)

a<-1



a1_temp=lm(crowdfunding$successful_rate~crowdfunding$GiniCoeff)
b1_temp=lm(crowdfunding$pAdDeg~crowdfunding$GiniCoeff)
cor.test(a1_temp$residuals,b1_temp$residuals)

a2_temp=lm(crowdfunding$successful_rate~crowdfunding$pAdDeg)
b2_temp=lm(crowdfunding$GiniCoeff~crowdfunding$pAdDeg)


se_s[1]
jsbl <- c("pAdDeg","successful_rate")   #要计算的相关系数的变量下标
tjbl <- c("GiniCoeff")  #条件(控制)变量的下标，即要排除影响的变量的下标
jsbl <- c("GiniCoeff","successful_rate")   #要计算的相关系数的变量下标
tjbl <- c("pAdDeg")  #条件(控制)变量的下标，即要排除影响的变量的下标
r<-pcor(c(jsbl,tjbl),cor(se))

q <- length(tjbl)   #计算要控制的变量数
n <- dim(se)[1]  #计算样本量

pcor_test.GiniCoeff <- pcor.test(r,q,n) #偏相关系数显著性检验结果（输出序号3）
r.GiniCoeff=r
pcor_test.GiniCoeff

pcor_test.pAdDeg <- pcor.test(r,q,n)
r.pAdDeg=r
pcor_test.pAdDeg
corr.test(se,use="pairwise")

```



```{r regression diagnostic}
summary(lm(crowdfunding$GiniCoeff~crowdfunding$pAdDeg))
cor.test(crowdfunding$GiniCoeff,crowdfunding$pAdDeg)



summary(lm(log(crowdfunding$GiniCoeff)~log(crowdfunding$pAdDeg)))
cor.test(log(crowdfunding$GiniCoeff),log(crowdfunding$pAdDeg))


qqplot(log(crowdfunding$successful_rate),log(crowdfunding$GiniCoeff))
qqplot(log(crowdfunding$successful_rate),log(crowdfunding$pAdDeg))

require(gvlma)
summary(gvlma(lm(log(crowdfunding$successful_rate)~log(crowdfunding$pAdDeg))))
summary(gvlma(lm(crowdfunding$successful_rate~crowdfunding$pAdDeg)))

summary(gvlma(lm(log(crowdfunding$successful_rate)~log(crowdfunding$GiniCoeff))))
summary(gvlma(lm(crowdfunding$successful_rate~crowdfunding$GiniCoeff)))

summary(lm(crowdfunding$successful_rate~crowdfunding$GiniCoeff))

par(mfrow=c(2,2))
plot(lm(log(crowdfunding$successful_rate)~log(crowdfunding$pAdDeg)))
plot(lm(crowdfunding$successful_rate~crowdfunding$pAdDeg))
plot(lm(crowdfunding$successful_rate~crowdfunding$GiniCoeff))

SR2Gini<-lm(crowdfunding$successful_rate~crowdfunding$GiniCoeff)
cor.test(crowdfunding$successful_rate,crowdfunding$GiniCoeff,method = "spearman")
summary(lm(SR2Gini$residuals~crowdfunding$pAdDeg))
cor.test(crowdfunding$successful_rate,crowdfunding$pAdDeg,method = "spearman")

summary(gvlma(lm(SR2Gini$residuals~crowdfunding$pAdDeg)))

SR2ad<-lm(crowdfunding$successful_rate~crowdfunding$pAdDeg)
cor.test(crowdfunding$successful_rate,crowdfunding$pAdDeg)
summary(lm(SR2ad$residuals~crowdfunding$GiniCoeff))
summary(gvlma(lm(SR2ad$residuals~crowdfunding$GiniCoeff)))

summary(gvlma(lm(crowdfunding$successful_rate~crowdfunding$GiniCoeff+crowdfunding$pAdDeg)))
require(car)

#SR2Gini---------
qqPlot(SR2Gini,labels=row.names(crowdfunding),id.method="identify",simulate=TRUE,main="Q-Q Plot")

influencePlot(SR2Gini,labels=row.names(crowdfunding),id.method="identify",main="Influence Plot", sub="Circle size if proportional to Cook's distance")  

#SR2all----------

SR2GiniAdedc<-lm(successful_rate~GiniCoeff+pAdDeg,data=crowdfunding)

qqPlot(SR2GiniAdedc,labels=row.names(crowdfunding),id.method="identify",simulate=TRUE,main="Q-Q Plot")
influencePlot(SR2GiniAdedc,labels=row.names(crowdfunding),id.method="identify",main="Influence Plot", sub="Circle size if proportional to Cook's distance")  
#
anova(SR2GiniAdedc)

summary(gvlma(lm(successful_rate~NPov000s,data=crowdfunding)))


SR2ginieduclevel<-lm(successful_rate~pAdDeg+PovRate1,data=crowdfunding)
summary(gvlma(SR2ginieduclevel))
logSR2ginieduclevel<-lm(log(successful_rate)~log(pAdDeg)+log(PovRate1),data=crowdfunding)
summary(gvlma(logSR2ginieduclevel))

test1<-lm(pAdDeg~PovRate1,data=crowdfunding)
test2<-lm(PovRate1~pAdDeg,data=crowdfunding)


plot(SR2ginieduclevel)
anova(SR2ginieduclevel)



#plot(crowdfunding$education_leverage,crowdfunding$successful_rate)
#boxplot(crowdfunding$successful_rate[crowdfunding$education_leverage==1],crowdfunding$successful_rate[crowdfunding$education_leverage==6])



#vif:Variance Inflation Factor----------
vif(SR2GiniAdedc)
vif(SR2ginieduclevel)


sqrt(vif(SR2GiniAdedc))>2
sqrt(vif(SR2ginieduclevel))>2
#PCA---------

prcomp(~pAdDeg+PovRate1,data=crowdfunding)


require(moments)
kurtosis(crowdfunding$successful_rate)
skewness(crowdfunding$successful_rate)

summary(gvlma(lm(crowdfunding$successful_rate~test1$residuals)))
summary(gvlma(lm(crowdfunding$successful_rate~test2$residuals)))

```
